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1.
Parks, Matthew.
Bayesian statistical inference of non-allelic homologous
recombination in the human genome using high-throughput sequencing
data.
Degree: PhD, Applied Mathematics, 2014, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:386252/
► Non-allelic homologous recombination (NAHR) plays a major role in genome rearrangement and is implicated in numerous genetic disorders. But detection of NAHR poses a serious…
(more)
▼ Non-allelic homologous recombination (NAHR) plays a
major role in genome rearrangement and is implicated in numerous
genetic disorders. But detection of NAHR poses a serious technical
challenge because its breakpoints occur in nearly identical regions
of highly homologous repeats. While a few structural variation
algorithms identify rearrangements in repeat regions, reliable
detection of NAHR remains out of reach. We present a probabilistic
model of NAHR and demonstrate its ability to find
previously-undetected NAHR rearrangements from low coverage
sequencing data. We identify a reliable subset of calls and discuss
their significance: segregation of NAHR in different populations,
effects on highly studied genes such as GBA and CYP2E1, and
associated features of NAHR.
Advisors/Committee Members: Lawrence, Charles (Director), Thompson, William (Reader), Raphael, Benjamin (Reader).
Subjects/Keywords: bayesian
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MLA ·
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APA (6th Edition):
Parks, M. (2014). Bayesian statistical inference of non-allelic homologous
recombination in the human genome using high-throughput sequencing
data. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:386252/
Chicago Manual of Style (16th Edition):
Parks, Matthew. “Bayesian statistical inference of non-allelic homologous
recombination in the human genome using high-throughput sequencing
data.” 2014. Doctoral Dissertation, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:386252/.
MLA Handbook (7th Edition):
Parks, Matthew. “Bayesian statistical inference of non-allelic homologous
recombination in the human genome using high-throughput sequencing
data.” 2014. Web. 05 Mar 2021.
Vancouver:
Parks M. Bayesian statistical inference of non-allelic homologous
recombination in the human genome using high-throughput sequencing
data. [Internet] [Doctoral dissertation]. Brown University; 2014. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:386252/.
Council of Science Editors:
Parks M. Bayesian statistical inference of non-allelic homologous
recombination in the human genome using high-throughput sequencing
data. [Doctoral Dissertation]. Brown University; 2014. Available from: https://repository.library.brown.edu/studio/item/bdr:386252/

University of Manitoba
2.
Kpekpena, Cynthia.
Bayesian analysis of binary and count data in two-arm trials.
Degree: Statistics, 2014, University of Manitoba
URL: http://hdl.handle.net/1993/23588
► Binary and count data naturally arise in clinical trials in health sciences. We consider a Bayesian analysis of binary and count data arising from two-arm…
(more)
▼ Binary and count data naturally arise in clinical trials in health sciences. We consider a
Bayesian analysis of binary and count data arising from two-arm clinical trials for testing hypotheses of equivalence.
For each type of data, we discuss the development of likelihood, the prior and the posterior distributions of parameters of interest. For binary data, we also examine the suitability of a normal approximation to the posterior distribution obtained via a Taylor series expansion.
When the posterior distribution is complex and high-dimensional, the
Bayesian inference is carried out using Markov Chain Monte Carlo (MCMC) methods. We also discuss a meta-analysis approach for data arising from two-arm trials with multiple studies. We assign a Dirichlet process prior for the study effects parameters for accounting heterogeneity among multiple studies. We illustrate the methods using actual data arising from several health studies.
Advisors/Committee Members: Muthukumarana, S (Statistics) (supervisor), Johnson, B (Statistics) Gumel, A(Mathematics) (examiningcommittee).
Subjects/Keywords: Bayesian
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
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APA (6th Edition):
Kpekpena, C. (2014). Bayesian analysis of binary and count data in two-arm trials. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/23588
Chicago Manual of Style (16th Edition):
Kpekpena, Cynthia. “Bayesian analysis of binary and count data in two-arm trials.” 2014. Masters Thesis, University of Manitoba. Accessed March 05, 2021.
http://hdl.handle.net/1993/23588.
MLA Handbook (7th Edition):
Kpekpena, Cynthia. “Bayesian analysis of binary and count data in two-arm trials.” 2014. Web. 05 Mar 2021.
Vancouver:
Kpekpena C. Bayesian analysis of binary and count data in two-arm trials. [Internet] [Masters thesis]. University of Manitoba; 2014. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1993/23588.
Council of Science Editors:
Kpekpena C. Bayesian analysis of binary and count data in two-arm trials. [Masters Thesis]. University of Manitoba; 2014. Available from: http://hdl.handle.net/1993/23588

University of Helsinki
3.
Cheng, Lu.
Bayesian methods in bacterial population genomics.
Degree: Department of Mathematics and Statistics, 2013, University of Helsinki
URL: http://hdl.handle.net/10138/40302
► Vast amounts of molecular data are being generated every day. However, how to properly harness the data remains often a challenge for many biologists. Firstly,…
(more)
▼ Vast amounts of molecular data are being generated every day. However, how to properly harness the data remains often a challenge for many biologists. Firstly, due to the typical large dimension of the molecular data, analyses can either require exhaustive amounts of computer memory or be very time-consuming, or both. Secondly, biological problems often have their own special features, which put demand on specially designed software to obtain meaningful results from statistical analyses without imposing too much requirements on the available computing resources. Finally, the general complexity of many biological research questions necessitates joint use of many different methods, which requires a considerable expertise in properly understanding the possibilities and limitations of the analysis tools.
In the first part of this thesis, we discuss three general Bayesian classification/clustering frameworks, which in the considered applications are targeted towards clustering of DNA sequence data, in particular in the context of bacterial population genomics and evolutionary epidemiology. Based on more generic Bayesian concepts, we have developed several statistical tools for analyzing DNA sequence data in bacterial metagenomics and population genomics.
In the second part of this thesis, we focus on discussing how to reconstruct bacterial evolutionary history from a combination of whole genome sequences and a number of core genes for which a large set of samples are available. A major problem is that for many bacterial species horizontal gene transfer of DNA, which is often termed as recombination, is relatively frequent and the recombined fragments within genome sequences have a tendency to severely distort the phylogenetic inferences. To obtain computationally viable solutions in practice for a majority of currently emerging genome data sets, it is necessary to divide the problem into parts and use different approaches in combination to perform the whole analysis. We demonstrate this strategy by application to two challenging data sets in the context of evolutionary epidemiology and show that biologically significant conclusions can be drawn by shedding light into the complex patterns of relatedness among strains of bacteria. Both studied organisms (it{Escherichia coli} and it{Campylobacter jejuni}) are major pathogens of humans and understanding the mechanisms behind the evolution of their populations is of vital importance for human health.
Although bacteria are everywhere in the earth, we still do not understand the small creatures very well. Due to the development of current sequencing technologies, we are able to use a DNA sequence to represent a bacteria. With lots of sequences collected, we can understand the relationships between different bacteria.
The first part of the thesis discusses three general classification frameworks to classify DNA sequences in different scenarios. For example, we have DNA sequences collected from bacteria in the environment. We want to know what are the collected…
Subjects/Keywords: bayesian statistics; bayesian statistics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Cheng, L. (2013). Bayesian methods in bacterial population genomics. (Doctoral Dissertation). University of Helsinki. Retrieved from http://hdl.handle.net/10138/40302
Chicago Manual of Style (16th Edition):
Cheng, Lu. “Bayesian methods in bacterial population genomics.” 2013. Doctoral Dissertation, University of Helsinki. Accessed March 05, 2021.
http://hdl.handle.net/10138/40302.
MLA Handbook (7th Edition):
Cheng, Lu. “Bayesian methods in bacterial population genomics.” 2013. Web. 05 Mar 2021.
Vancouver:
Cheng L. Bayesian methods in bacterial population genomics. [Internet] [Doctoral dissertation]. University of Helsinki; 2013. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/10138/40302.
Council of Science Editors:
Cheng L. Bayesian methods in bacterial population genomics. [Doctoral Dissertation]. University of Helsinki; 2013. Available from: http://hdl.handle.net/10138/40302

University of Oxford
4.
Gray-Davies, Tristan Daniel.
Scalable Bayesian regression utilising marginal information.
Degree: PhD, 2017, University of Oxford
URL: https://ora.ox.ac.uk/objects/uuid:ad69017a-57fb-4b54-bc82-9e564796f55f
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729396
► This thesis explores approaches to regression that utilise the treatment of covariates as random variables. The distribution of covariates, along with the conditional regression model…
(more)
▼ This thesis explores approaches to regression that utilise the treatment of covariates as random variables. The distribution of covariates, along with the conditional regression model Y | X, define the joint model over (Y,X), and in particular, the marginal distribution of the response Y. This marginal distribution provides a vehicle for the incorporation of prior information, as well as external, marginal data. The marginal distribution of the response provides a means of parameterisation that can yield scalable inference, simple prior elicitation, and, in the case of survival analysis, the complete treatment of truncated data. In many cases, this information can be utilised without need to specify a model for X. Chapter 2 considers the application of Bayesian linear regression where large marginal datasets are available, but the collection of response and covariate data together is limited to a small dataset. These marginal datasets can be used to estimate the marginal means and variances of Y and X, which impose two constraints on the parameters of the linear regression model. We define a joint prior over covariate effects and the conditional variance σ2 via a parameter transformation, which allows us to guarantee these marginal constraints are met. This provides a computationally efficient means of incorporating marginal information, useful when incorporation via the imputation of missing values may be implausible. The resulting prior and posterior have rich dependence structures that have a natural 'analysis of variance' interpretation, due to the constraint on the total marginal variance of Y. The concept of 'marginal coherence' is introduced, whereby competing models place the same prior on the marginal mean and variance of the response. Our marginally constrained prior can be extended by placing priors on the marginal variances, in order to perform variable selection in a marginally coherent fashion. Chapter 3 constructs a Bayesian nonparametric regression model parameterised in terms of FY , the marginal distribution of the response. This naturally allows the incorporation of marginal data, and provides a natural means of specifying a prior distribution for a regression model. The construction is such that the distribution of the ordering of the response, given covariates, takes the form of the Plackett-Luce model for ranks. This facilitates a natural composite likelihood approximation that decomposes the likelihood into a term for the marginal response data, and a term for the probability of the observed ranking. This can be viewed as a extension to the partial likelihood for proportional hazards models. This convenient form leads to simple approximate posterior inference, which circumvents the need to perform MCMC, allowing scalability to large datasets. We apply the model to a US Census dataset with over 1,300,000 data points and more than 100 covariates, where the nonparametric prior is able to capture the highly non-standard distribution of incomes. Chapter 4 explores the analysis of…
Subjects/Keywords: 519.5; Regression; Bayesian; Bayesian Nonparametrics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gray-Davies, T. D. (2017). Scalable Bayesian regression utilising marginal information. (Doctoral Dissertation). University of Oxford. Retrieved from https://ora.ox.ac.uk/objects/uuid:ad69017a-57fb-4b54-bc82-9e564796f55f ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729396
Chicago Manual of Style (16th Edition):
Gray-Davies, Tristan Daniel. “Scalable Bayesian regression utilising marginal information.” 2017. Doctoral Dissertation, University of Oxford. Accessed March 05, 2021.
https://ora.ox.ac.uk/objects/uuid:ad69017a-57fb-4b54-bc82-9e564796f55f ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729396.
MLA Handbook (7th Edition):
Gray-Davies, Tristan Daniel. “Scalable Bayesian regression utilising marginal information.” 2017. Web. 05 Mar 2021.
Vancouver:
Gray-Davies TD. Scalable Bayesian regression utilising marginal information. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2021 Mar 05].
Available from: https://ora.ox.ac.uk/objects/uuid:ad69017a-57fb-4b54-bc82-9e564796f55f ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729396.
Council of Science Editors:
Gray-Davies TD. Scalable Bayesian regression utilising marginal information. [Doctoral Dissertation]. University of Oxford; 2017. Available from: https://ora.ox.ac.uk/objects/uuid:ad69017a-57fb-4b54-bc82-9e564796f55f ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729396
5.
Doraiswamy, Srikrishna.
Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach.
Degree: 2013, Texas Digital Library
URL: http://hdl.handle.net/1969;
http://hdl.handle.net/2249.1/66804
► The aim of the current work is to develop a Bayesian approach to model and simulate the behavior of materials with nonlinear mechanical response in…
(more)
▼ The aim of the current work is to develop a
Bayesian approach to model and simulate the behavior of materials with nonlinear mechanical response in the presence of significant uncertainties in the experimental data as well as the applicability of models. The core idea of this approach is to combine deterministic approaches by the use of physics based models, with ideas from
Bayesian inference to account for such uncertainties.
Traditionally, parameters of models in mechanics have been identified through deterministic approaches to obtain single point estimates. Such methods perform very well for linear models and are the preferred approach in identifying model parameters, especially for precisely engineered systems such as structures and machinery. But in the presence of large variations such as in the response of biological materials, such deterministic approaches do not sufficiently capture the uncertainty in the response. We propose that the model parameters need to encode the spread that is observed in the data in addition to modeling the physics of the system. To this end, we propose the idea of probability distributions for model parameters in order to incorporate the uncertainty in the data.
We demonstrate this probabilistic approach to identifying model parameters with the example of two problems: the characterization of sheep arteries using data from inflation experiments and the problem of detecting an inhomogeneity in a cantilever beam. The parameters in the artery characterization problem are the model parameters in the constitutive models and in the cantilever problem the parameters are the stiffnesses of the inhomogeneity and the material of the beam. For each of these problems, we compute the probability distribution of the parameters using
Bayesian inference.
We show that the probability distributions of parameters can be used towards two kinds of diagnostics: assigning probability to a hypothesis (inhomogeneity detection problem) and using the probability distribution for classifying newly obtained data (characterization of artery data). For the inhomogeneity detection problem, the hypothesis is a statement on the ratio of the stiffnesses and it is observed that the probability of the hypothesis matches well with the data. In the case of the artery characterization problem, new data was successfully classified using the probability distributions computed with training data.
Advisors/Committee Members: Srinivasa, Arun R (advisor).
Subjects/Keywords: Bayesian inference
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Doraiswamy, S. (2013). Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach. (Thesis). Texas Digital Library. Retrieved from http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Doraiswamy, Srikrishna. “Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach.” 2013. Thesis, Texas Digital Library. Accessed March 05, 2021.
http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Doraiswamy, Srikrishna. “Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach.” 2013. Web. 05 Mar 2021.
Vancouver:
Doraiswamy S. Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach. [Internet] [Thesis]. Texas Digital Library; 2013. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Doraiswamy S. Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach. [Thesis]. Texas Digital Library; 2013. Available from: http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
6.
Karakci, Ata.
BAYESIAN ANALYSIS OF SYSTEMATIC EFFECTS IN INTERFEROMETRIC
OBSERVATIONS OF THE COSMIC MICROWAVE BACKGROUND
POLARIZATION.
Degree: PhD, Physics, 2014, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:386173/
► The detection of the primordial B-mode spectrum of the polarized cosmic microwave background (CMB) signal may provide a probe for inflation. However, the B-mode signal…
(more)
▼ The detection of the primordial B-mode spectrum of the
polarized cosmic microwave background (CMB) signal may provide a
probe for inflation. However, the B-mode signal is orders of
magnitude weaker than the temperature and E-mode signals.
Observation of such faint signals requires excellent control of
systematic errors. Interferometers may have certain advantages over
imaging experiments in the control of systematic effects. Current
and future high resolution cosmology observations, such as those
that will be carried out by the QU Bolometric Interferometer for
Cosmology (QUBIC), require computationally efficient data analysis
techniques. Since the observed CMB signal can be interpreted as a
single realization of a random process, CMB data is most suitably
analyzed in a
Bayesian, rather than frequentist, approach. In
comparison to alternative methods of extracting power spectra, such
as maximum likelihood and pseudo-Cl estimators, the method of Gibbs
sampling has the advantage of providing simultaneous inference of
power spectrum and signal with similar computational complexity. We
demonstrate the application of Gibbs sampling to realistic
interferometric observations, including an incomplete uv-coverage,
finite beam size and baseline-dependent noise, of polarized
signals. We present a complete simulation pipeline of
interferometric observations of the CMB polarization to understand
the effects of systematic errors. A realistic, QUBIC-like
interferometer design is simulated with systematics that
incorporate the effects of sky rotation. Several types of
systematic errors are considered including antenna pointing, gain
and coupling errors as well as beam cross-polarization and shape
errors. The simulated data sets are analyzed by both the maximum
likelihood method and the method of Gibbs sampling. The results
from both methods have been found to be consistent with each other,
as well as with the analytical estimations. Our simulations
determine the required levels of control of systematic effects for
a QUBIC-like interferometer, which targets the B-mode polarization
signal. We show that the method of Gibbs sampling naturally extends
to include a
Bayesian foreground separation technique. We also
demonstrate that the method can be further generalized to 3D power
spectrum inference from interferometric data of the redshifted 21
cm HI line.
Advisors/Committee Members: Tucker, Gregory (Director), Dell’Antonio, Ian (Reader), Koushiappas, Savvas (Reader).
Subjects/Keywords: bayesian analysis
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Karakci, A. (2014). BAYESIAN ANALYSIS OF SYSTEMATIC EFFECTS IN INTERFEROMETRIC
OBSERVATIONS OF THE COSMIC MICROWAVE BACKGROUND
POLARIZATION. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:386173/
Chicago Manual of Style (16th Edition):
Karakci, Ata. “BAYESIAN ANALYSIS OF SYSTEMATIC EFFECTS IN INTERFEROMETRIC
OBSERVATIONS OF THE COSMIC MICROWAVE BACKGROUND
POLARIZATION.” 2014. Doctoral Dissertation, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:386173/.
MLA Handbook (7th Edition):
Karakci, Ata. “BAYESIAN ANALYSIS OF SYSTEMATIC EFFECTS IN INTERFEROMETRIC
OBSERVATIONS OF THE COSMIC MICROWAVE BACKGROUND
POLARIZATION.” 2014. Web. 05 Mar 2021.
Vancouver:
Karakci A. BAYESIAN ANALYSIS OF SYSTEMATIC EFFECTS IN INTERFEROMETRIC
OBSERVATIONS OF THE COSMIC MICROWAVE BACKGROUND
POLARIZATION. [Internet] [Doctoral dissertation]. Brown University; 2014. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:386173/.
Council of Science Editors:
Karakci A. BAYESIAN ANALYSIS OF SYSTEMATIC EFFECTS IN INTERFEROMETRIC
OBSERVATIONS OF THE COSMIC MICROWAVE BACKGROUND
POLARIZATION. [Doctoral Dissertation]. Brown University; 2014. Available from: https://repository.library.brown.edu/studio/item/bdr:386173/
7.
Sugden, Lauren Alpert.
Structure, Variation, and Reproducibility: Bayesian
inference in problems arising from the study of RNA and an
RNA-binding protein.
Degree: PhD, Applied Mathematics, 2014, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:386269/
► Far from being solely a passive messenger between DNA and protein, RNA is a complex molecule involved in regulation at many levels. While the best-known…
(more)
▼ Far from being solely a passive messenger between DNA
and protein, RNA is a complex molecule involved in regulation at
many levels. While the best-known RNAs, messenger RNAs (mRNA), do
exist to carry information, there is also a large population of
non-coding RNAs that can take on complicated structures that enable
them to perform functions throughout the cell. Even mRNAs are not
static molecules, however. Mechanisms for altering mRNA sequences
result in transcripts that are no longer faithful representations
of the information in the genome. One such mechanism is RNA editing
by ADAR, which binds double-stranded RNA and targets a particular
adenosine for conversion to inosine, which is recognized by the
cell as guanosine. A major consequence of editing is amino-acid
recoding, resulting in protein diversification. In this thesis, we
look at three problems motivated by the study of RNA and ADAR.
First, we propose a method for assessing the reproducibility of
genome-scale studies that make a large number of predictions, using
the prediction of ADAR binding sites as a motivating example. We
then address the problem of avoiding false positives when
identifying subtle signals such as ADAR modifications in
high-throughput sequencing data in the presence of genetic
polymorphisms specific to laboratory populations. Finally, we turn
to structural prediction of RNA, inferring the common structural
and sequence characteristics of a set of related
transcripts.
Advisors/Committee Members: Lawrence, Charles (Director), Thompson, William (Reader), Reenan, Robert (Reader).
Subjects/Keywords: Bayesian inference
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sugden, L. A. (2014). Structure, Variation, and Reproducibility: Bayesian
inference in problems arising from the study of RNA and an
RNA-binding protein. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:386269/
Chicago Manual of Style (16th Edition):
Sugden, Lauren Alpert. “Structure, Variation, and Reproducibility: Bayesian
inference in problems arising from the study of RNA and an
RNA-binding protein.” 2014. Doctoral Dissertation, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:386269/.
MLA Handbook (7th Edition):
Sugden, Lauren Alpert. “Structure, Variation, and Reproducibility: Bayesian
inference in problems arising from the study of RNA and an
RNA-binding protein.” 2014. Web. 05 Mar 2021.
Vancouver:
Sugden LA. Structure, Variation, and Reproducibility: Bayesian
inference in problems arising from the study of RNA and an
RNA-binding protein. [Internet] [Doctoral dissertation]. Brown University; 2014. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:386269/.
Council of Science Editors:
Sugden LA. Structure, Variation, and Reproducibility: Bayesian
inference in problems arising from the study of RNA and an
RNA-binding protein. [Doctoral Dissertation]. Brown University; 2014. Available from: https://repository.library.brown.edu/studio/item/bdr:386269/
8.
Miller, Jeffrey W.
Nonparametric and Variable-Dimension Bayesian Mixture
Models: Analysis, Comparison, and New Methods.
Degree: PhD, Applied Mathematics, 2014, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:386270/
► Many nonparametric Bayesian models can be viewed as an infinite-dimensional limit of a family of finite-dimensional models. However, another way to construct a flexible Bayesian…
(more)
▼ Many nonparametric
Bayesian models can be viewed as an
infinite-dimensional limit of a family of finite-dimensional
models. However, another way to construct a flexible
Bayesian model
is to take the same family and put a prior on the dimension –
that is, to use a variable-dimension model – for example, putting
a prior on the number of components in a finite mixture. Using
theory and experiments, this thesis analyzes some of the
differences and similarities between the nonparametric and
variable-dimension approaches, develops new inference algorithms
for these models, and explores new variable-dimension models.
Primarily, we focus on the Dirichlet process mixture (DPM) and a
variable-dimension alternative that we refer to as the mixture of
finite mixtures (MFM) model. One of the main differences between
DPMs and MFMs is the behavior of the posterior on the number of
clusters. We show that for a large class of nonparametric mixtures,
including DPMs and Pitman – Yor process mixtures over a wide range
of families of component distributions, the posterior on the number
of clusters does not concentrate at the true number of components
when the data comes from a finite mixture. Meanwhile, it is known
that the MFM posterior on the number of components concentrates at
the true number, assuming the model is correctly specified. We
explore the properties of the MFM, finding that it has many of the
same attractive features as the DPM: a simple partition
distribution, exchangeability properties, restaurant process,
random discrete measure representation, and in certain special
cases, a simple stick-breaking representation. As a result, many of
the same approximate inference algorithms used for nonparametric
mixtures can be easily adapted to the MFM. We also propose two new
variable-dimension models: the hierarchical mixture of finite
mixtures (HMFM) as an alternative to the hierarchical Dirichlet
process (HDP), and the mixture of finite feature models (MFFM) as
an alternative to the Indian buffet process (IBP). As with the MFM,
these variable-dimension models exhibit some of the same appealing
characteristics as their nonparametric counterparts.
Advisors/Committee Members: Harrison, Matthew (Director), Geman, Stuart (Reader), MacEachern, Steven (Reader).
Subjects/Keywords: Bayesian nonparametrics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Miller, J. W. (2014). Nonparametric and Variable-Dimension Bayesian Mixture
Models: Analysis, Comparison, and New Methods. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:386270/
Chicago Manual of Style (16th Edition):
Miller, Jeffrey W. “Nonparametric and Variable-Dimension Bayesian Mixture
Models: Analysis, Comparison, and New Methods.” 2014. Doctoral Dissertation, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:386270/.
MLA Handbook (7th Edition):
Miller, Jeffrey W. “Nonparametric and Variable-Dimension Bayesian Mixture
Models: Analysis, Comparison, and New Methods.” 2014. Web. 05 Mar 2021.
Vancouver:
Miller JW. Nonparametric and Variable-Dimension Bayesian Mixture
Models: Analysis, Comparison, and New Methods. [Internet] [Doctoral dissertation]. Brown University; 2014. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:386270/.
Council of Science Editors:
Miller JW. Nonparametric and Variable-Dimension Bayesian Mixture
Models: Analysis, Comparison, and New Methods. [Doctoral Dissertation]. Brown University; 2014. Available from: https://repository.library.brown.edu/studio/item/bdr:386270/
9.
Gu, Chenyang.
Statistical Missing Data Methods with Applications to Health
Services Research.
Degree: Biostatistics, 2016, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:730655/
► This dissertation presents three statistical missing data methods with applications to health services research. The assessment of patients' functional status across the continuum of care…
(more)
▼ This dissertation presents three statistical missing
data methods with applications to health services research. The
assessment of patients' functional status across the continuum of
care typically requires a common patient assessment tool. However,
assessment tools that are used in various health care settings
differ and cannot be easily contrasted. In the first chapter, we
propose a procedure called latent variable matching (LVM) that
views equating setting-specific health assessment questionnaires as
a missing data problem and combines Item Response Theory (IRT)
models with multiple imputation to impute unmeasured assessments.
LVM is a hot deck imputation method in which missing assessments of
a patient are replaced with observed responses from a patient with
similar underlying functional status estimated using IRT models.
LVM provides statistically valid and robust inferences, and
overcomes several limitations of the existing methods. In the
second chapter, we proposed a two-stage procedure that combines
nested multiple imputation with a multivariate ordinal probit
(MVOP) model to obtain a common patient assessment scale across the
continuum of care via imputing unmeasured assessments at multiple
time points after patients are discharged from IRFs. This procedure
enables evaluation and comparison of the rates of functional
improvement in post-acute settings using a common measure. To
generate multiple imputations using the MVOP model, both
likelihood-based methods and fully
Bayesian methods are developed.
In the third chapter, we present an alternative approach to
translate functional assessments across the continuum of care. The
proposed procedure is also a hot deck imputation method. We propose
a
Bayesian shared latent variable model to estimate patients'
underlying functional status. The estimated underlying functional
status at multiple time points are matched based on a Mahalanobis
distance to identify "similar" patients whose observed responses
are used to fill in the unmeasured assessments. We applied the
proposed procedures to analyze a data set of patients who had a
stroke and were either discharged home or to SNFs after discharge
from IRFs, and compared the rates of functional change experienced
by patients treated in different health care
settings.
Advisors/Committee Members: Gutman, Roee (Director), Gatsonis, Constantine (Reader), Mor, Vincent (Reader), Harel, Ofer (Reader).
Subjects/Keywords: Bayesian methods
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gu, C. (2016). Statistical Missing Data Methods with Applications to Health
Services Research. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:730655/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Gu, Chenyang. “Statistical Missing Data Methods with Applications to Health
Services Research.” 2016. Thesis, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:730655/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Gu, Chenyang. “Statistical Missing Data Methods with Applications to Health
Services Research.” 2016. Web. 05 Mar 2021.
Vancouver:
Gu C. Statistical Missing Data Methods with Applications to Health
Services Research. [Internet] [Thesis]. Brown University; 2016. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:730655/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Gu C. Statistical Missing Data Methods with Applications to Health
Services Research. [Thesis]. Brown University; 2016. Available from: https://repository.library.brown.edu/studio/item/bdr:730655/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
10.
Kim, Daeil.
Scalable Bayesian Nonparametric Models for Networks and
Documents.
Degree: Department of Computer Science, 2016, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:733381/
► We develop Bayesian nonparametric statistical models of document collections and social networks. Extending classic parametric topic models of documents, and stochastic block models of networks,…
(more)
▼ We develop
Bayesian nonparametric statistical models
of document collections and social networks. Extending classic
parametric topic models of documents, and stochastic block models
of networks, we first formulate flexible
Bayesian nonparametric
models based on the logistic stick-breaking process. This prior
allows our model to automatically learn the dimension of the latent
structure, use observed metadata to influence this structure, and
discover correlations that exist between them. We call this model
the Doubly Correlated Nonparametric Model (DCNM), and derive
efficient MCMC learning algorithms. We then focus on the problem of
scaling inference to large networks. We propose a hierarchical
Dirichlet Process (HDP) relational model and derive a structured
variational inference algorithm. For the practically important case
of communities with assortative structure, we derive new updates
where inference scales linearly in time and memory with the number
of active clusters. From this, we develop a stochastic variational
approach that allows us to scale inference to networks that contain
tens of thousands of nodes. Finally, we develop pruning techniques
that allow us to dynamically shrink the number of communities, and
effective strategies for specifying learning rate parameters. After
developing scalable inference models for relational data, we
develop a memoized variational inference algorithm for the HDP
topic model. This approach provides a more scalable framework for
comparing models of varying complexity, by caching sufficient
statistics of small batches of a very large dataset. Elegant
delete-merge moves are then derived to optimize rigorous lower
bounds on the marginal likelihood of the data, avoiding
approximations required by previous stochastic inference
algorithms. We use our memoized variational inference algorithms to
develop Refinery, an open-source web platform for topic modeling
that allows non-technical experts to leverage the power of topic
models.
Advisors/Committee Members: Sudderth, Erik B (Advisor), Charniak, Eugene (Reader), Blei, David M (Reader).
Subjects/Keywords: Bayesian Nonparametrics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kim, D. (2016). Scalable Bayesian Nonparametric Models for Networks and
Documents. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:733381/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kim, Daeil. “Scalable Bayesian Nonparametric Models for Networks and
Documents.” 2016. Thesis, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:733381/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kim, Daeil. “Scalable Bayesian Nonparametric Models for Networks and
Documents.” 2016. Web. 05 Mar 2021.
Vancouver:
Kim D. Scalable Bayesian Nonparametric Models for Networks and
Documents. [Internet] [Thesis]. Brown University; 2016. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:733381/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kim D. Scalable Bayesian Nonparametric Models for Networks and
Documents. [Thesis]. Brown University; 2016. Available from: https://repository.library.brown.edu/studio/item/bdr:733381/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
11.
Lin, Luan.
Bayesian Inference and High-D space Characterization with
application in Paleoclimatology and Biology.
Degree: PhD, Applied Mathematics, 2012, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:297525/
► This thesis is a mathematical study of paleoclimatology and computational biology. Part I gives an introduction and overview of this dissertation. Part II presents the…
(more)
▼ This thesis is a mathematical study of
paleoclimatology and computational biology.
Part I gives an introduction and overview of this
dissertation.
Part II presents the applications of hidden Markov model in
paleoclimatology study.
Chapter 1 employs a two state hidden Markov model with
multivariate emission to challenge the assumption that ENSO-like
variability dominated Peru margin oceanography on decadal and
longer time scales over the Holocene epoch. The HMM result shows
that two regimes of variability dominate, one shows strong
correlations between surface and subsurface proxies while the other
does not.
Chapter 2 is another application of hidden Markov model in
paleoclimatology. A pair hidden Markov model is built to implement
the alignments between pairs of stratigraphic records and provide
uncertainty analysis. In addition to the most probable alignment,
centroid alignment is also investigated.
Part III focuses on characterization of high dimensional
space.
Chapter 3 develops an iterative algorithm to characterize the
high dimensional spaces by identifying the most informative
variables through mutual information based criteria. Application to
the posterior space of RNA secondary structure is presented.
Chapter 4 proposes a novel model based clustering algorithm
based on L
p distance in the situations where not only samples
but also corresponding densities/probabilities (or
subject to a
normalization constant) are available. We demonstrate the abilities
of this approach by experimenting via Gaussian Mixture Models.
Part IV makes some conclusions and suggests future
directions.
Advisors/Committee Members: Lawrence, Charles (Director), Herbert, Timothy (Reader), Thompson, William (Reader).
Subjects/Keywords: bayesian inference
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lin, L. (2012). Bayesian Inference and High-D space Characterization with
application in Paleoclimatology and Biology. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:297525/
Chicago Manual of Style (16th Edition):
Lin, Luan. “Bayesian Inference and High-D space Characterization with
application in Paleoclimatology and Biology.” 2012. Doctoral Dissertation, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:297525/.
MLA Handbook (7th Edition):
Lin, Luan. “Bayesian Inference and High-D space Characterization with
application in Paleoclimatology and Biology.” 2012. Web. 05 Mar 2021.
Vancouver:
Lin L. Bayesian Inference and High-D space Characterization with
application in Paleoclimatology and Biology. [Internet] [Doctoral dissertation]. Brown University; 2012. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:297525/.
Council of Science Editors:
Lin L. Bayesian Inference and High-D space Characterization with
application in Paleoclimatology and Biology. [Doctoral Dissertation]. Brown University; 2012. Available from: https://repository.library.brown.edu/studio/item/bdr:297525/
12.
Hughes, Michael C.
Reliable and scalable variational inference for
nonparametric mixtures, topics, and sequences.
Degree: PhD, Computer Science, 2016, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:674327/
► We develop new algorithms for training nonparametric clustering models based on the Dirichlet Process (DP), including DP mixture models, hierarchical Dirichlet process (HDP) topic models,…
(more)
▼ We develop new algorithms for training nonparametric
clustering models based on the Dirichlet Process (DP), including DP
mixture models, hierarchical Dirichlet process (HDP) topic models,
and HDP hidden Markov models. These
Bayesian nonparametric models
allow coherent comparisons of different clusterings of a given
dataset. The nonparametric approach is particularly promising for
large-scale applications, where other model selection techniques
like cross-validation are too expensive. However, existing training
algorithms fail to live up to this promise. Both Monte Carlo
samplers and variational optimization methods are vulnerable to
local optima and sensitive to initialization, especially the
initial number of clusters. Our new algorithms can reliably escape
poor initializations to discover interpretable clusters from
millions of training examples. For the DP mixture model, we pose a
variational optimization problem in which the number of
instantiated clusters assigned to data can be adapted during
training. The focus of this optimization is an objective function
which tightly lower bounds the marginal likelihood and thus can be
used for
Bayesian model selection. Our algorithm maximizes this
objective score via block coordinate ascent interleaved with
proposal moves that can add useful clusters to escape local optima
while removing redundant or irrelevant clusters. We further
introduce an incremental algorithm that can exactly optimize our
objective function on large datasets while processing only small
batches at each step. Our approach uses cached or memoized
sufficient statistics to make exact decisions for proposal
acceptance or rejection. This memoized approach has the same
runtime cost as previous stochastic methods but allows exact
acceptance decisions for cluster proposals and avoids learning
rates entirely. We later extend these algorithms to HDP topic
models and HDP hidden Markov models. Previous methods for the HDP
have used zero-variance point estimates with problematic model
selection properties. Instead, we find sophisticated solutions to
the non-conjugacy inherent in the HDP that still yield an
optimization objective function usable for
Bayesian model
selection. We demonstrate promising proposal moves for adapting the
number of clusters during memoized training on millions of news
articles, hundreds of motion capture sequences, and the human
genome.
Advisors/Committee Members: Sudderth, Erik (Director), Raphael, Benjamin (Reader), Fox, Emily (Reader).
Subjects/Keywords: Bayesian nonparametrics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hughes, M. C. (2016). Reliable and scalable variational inference for
nonparametric mixtures, topics, and sequences. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:674327/
Chicago Manual of Style (16th Edition):
Hughes, Michael C. “Reliable and scalable variational inference for
nonparametric mixtures, topics, and sequences.” 2016. Doctoral Dissertation, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:674327/.
MLA Handbook (7th Edition):
Hughes, Michael C. “Reliable and scalable variational inference for
nonparametric mixtures, topics, and sequences.” 2016. Web. 05 Mar 2021.
Vancouver:
Hughes MC. Reliable and scalable variational inference for
nonparametric mixtures, topics, and sequences. [Internet] [Doctoral dissertation]. Brown University; 2016. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:674327/.
Council of Science Editors:
Hughes MC. Reliable and scalable variational inference for
nonparametric mixtures, topics, and sequences. [Doctoral Dissertation]. Brown University; 2016. Available from: https://repository.library.brown.edu/studio/item/bdr:674327/
13.
Ghosh, Soumya.
Bayesian Nonparametric Discovery of Layers and Parts from
Scenes and Objects.
Degree: PhD, Computer Science, 2015, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:419552/
► We develop statistical methods for analyzing natural images, videos, motion capture (MoCap) sequences, and three-dimensional (3D) representations of articulated objects. Our goal is to discover…
(more)
▼ We develop statistical methods for analyzing natural
images, videos, motion capture (MoCap) sequences, and
three-dimensional (3D) representations of articulated objects. Our
goal is to discover and characterize regions, objects, actions, and
the parts composing them. Such data typically exhibit wide
variability in complexity, with some instances containing only a
few objects (parts) and others exhibiting complex structure.
Further, images and 3D object representations have strong spatial
correlations, while MoCap and video sequences additionally exhibit
temporal dependencies. Effective models for such data must
automatically reason about the number of constituent objects and
parts, while simultaneously modeling strong spatio-temporal
interactions. Motivated by these challenges, we study and extend
flexible
Bayesian nonparametric priors. Focusing first on images,
we explore a family of models that generalize the Pitman-Yor (PY)
process to produce decompositions of images into depth-ordered
segments (layers). Spatial correlations are captured through an
ordered set of Gaussian processes that encourage piecewise smooth
allocation of pixels to segments. We develop variational methods
for effective learning and robust inference, and demonstrate
competitive performance on standard image segmentation benchmarks.
Next, we explore the distance dependent Chinese restaurant process
(ddCRP), a distribution over partitions that allows user-specified
affinity functions to capture dependencies between data instances.
We show that a statistical model endowed with a ddCRP prior, and an
expressive likelihood for modeling deformations, produces
state-of-the-art segmentations of articulated 3D objects. We then
develop a family of hierarchical ddCRP priors that allow
dependencies both between data instances and their latent clusters.
Coupled with vector auto-regressive likelihoods, this hierarchical
ddCRP successfully discovers activities from related MoCap
sequences. The performance of the distance dependent models
crucially depends on the choice of the affinity functions.
Designing functions that capture appropriate domain specific
dependencies can be challenging. We develop extensions to the
distance dependent models and borrow ideas from the approximate
Bayesian computation (ABC) literature to develop algorithms for
learning affinity functions from human annotated data. Through
extensive experiments on image and video segmentation corpuses, we
demonstrate that the learned models consistently outperform their
hand-crafted counterparts.
Advisors/Committee Members: Sudderth, Erik (Director), Hays, James (Reader), Black, Michael (Reader).
Subjects/Keywords: Bayesian nonparametrics
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ghosh, S. (2015). Bayesian Nonparametric Discovery of Layers and Parts from
Scenes and Objects. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:419552/
Chicago Manual of Style (16th Edition):
Ghosh, Soumya. “Bayesian Nonparametric Discovery of Layers and Parts from
Scenes and Objects.” 2015. Doctoral Dissertation, Brown University. Accessed March 05, 2021.
https://repository.library.brown.edu/studio/item/bdr:419552/.
MLA Handbook (7th Edition):
Ghosh, Soumya. “Bayesian Nonparametric Discovery of Layers and Parts from
Scenes and Objects.” 2015. Web. 05 Mar 2021.
Vancouver:
Ghosh S. Bayesian Nonparametric Discovery of Layers and Parts from
Scenes and Objects. [Internet] [Doctoral dissertation]. Brown University; 2015. [cited 2021 Mar 05].
Available from: https://repository.library.brown.edu/studio/item/bdr:419552/.
Council of Science Editors:
Ghosh S. Bayesian Nonparametric Discovery of Layers and Parts from
Scenes and Objects. [Doctoral Dissertation]. Brown University; 2015. Available from: https://repository.library.brown.edu/studio/item/bdr:419552/
14.
Davis, Bryant Frost.
Constructing a Bayesian Spatial Presence-Absence Model for Animals in the Serengeti National Park.
Degree: 2016, Wake Forest University
URL: http://hdl.handle.net/10339/59266
► Ecologists have been spearheading the Snapshot Serengeti project in Tanzania's Serengeti National Park for the past few years, a large camera trap project with the…
(more)
▼ Ecologists have been spearheading the Snapshot Serengeti project in Tanzania's Serengeti National Park for the past few years, a large camera trap project with the intent of discovering more about how animals interact with their environment. We fit a hierarchical logistic model to predict the presence or absence of a particular species in a specific area using environmental covariates. Additionally, we utilized centered spatially dependent terms; a term accounting for species dependence from neighboring sites, and a term accounting for cross-species dependence from neighboring sites. Our model also includes a latent variable for detectability; the model accounts for the fact that we are working with imperfectly observed data by including a latent variable for detectability. We set out to investigate a few questions. First, what is the relationship between body size and attraction to high NDVI levels? Second, are herbivores more likely to avoid areas where they have recently seen lions or where lions have historically visited? Third, what are the different cross-species dependence levels? Our model has currently only been applied to pairs of species simultaneously, but something that makes it different is the fact that, aside from computational difficulty, nothing prevents it from running as many species as we want at once and seeing how they interact with each other.
Subjects/Keywords: Bayesian
…common to use an autologistic model to predict presence or absence within a
Bayesian…
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Davis, B. F. (2016). Constructing a Bayesian Spatial Presence-Absence Model for Animals in the Serengeti National Park. (Thesis). Wake Forest University. Retrieved from http://hdl.handle.net/10339/59266
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Davis, Bryant Frost. “Constructing a Bayesian Spatial Presence-Absence Model for Animals in the Serengeti National Park.” 2016. Thesis, Wake Forest University. Accessed March 05, 2021.
http://hdl.handle.net/10339/59266.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Davis, Bryant Frost. “Constructing a Bayesian Spatial Presence-Absence Model for Animals in the Serengeti National Park.” 2016. Web. 05 Mar 2021.
Vancouver:
Davis BF. Constructing a Bayesian Spatial Presence-Absence Model for Animals in the Serengeti National Park. [Internet] [Thesis]. Wake Forest University; 2016. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/10339/59266.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Davis BF. Constructing a Bayesian Spatial Presence-Absence Model for Animals in the Serengeti National Park. [Thesis]. Wake Forest University; 2016. Available from: http://hdl.handle.net/10339/59266
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
15.
Patton, Kristopher Laurence.
Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data.
Degree: 2012, Wake Forest University
URL: http://hdl.handle.net/10339/37257
► Biological experiments of proteins and genes often involve the collection of multiple replicates of sparse time-course data. From such time-course data, protein (or gene) interaction…
(more)
▼ Biological experiments of proteins and genes often involve the collection of multiple replicates of sparse time-course data. From such time-course data, protein (or gene) interaction posterior probabilities are computed based on individual and multiple replicates. This is accomplished through Bayesian inference in conjunction with the Metropolis-Hastings algorithm. The Bayesian posterior probability is computed for two distinct cases. One case assumes the replicates are independent events, the other assumes the replicates are not independent events (using a hierarchical structure). Closed form Bayes factors are developed for each situation. In order to test the algorithm's ability to identify signal, multiple replicates of simulated network data are generated and modeled. Two biological data sets, Arabidopsis thaliana and PC-3, are also modeled, each consisting of multiple replicates. For multiple replicates, modeling is done in accordance with the afore mentioned independence and non-independence assumptions among replicates. Models are also produced for individual replicates. Our algorithms produce high protein (or gene) interaction posterior probabilities to pairs of proteins when they have at least moderate partial correlation.
Subjects/Keywords: Bayesian
…and multiple
replicates. This is accomplished through Bayesian inference in conjunction with… …the
Metropolis-Hastings algorithm. The Bayesian posterior probability is computed for
two… …probabilistically from a
Bayesian perspective. Modeling of protein interactions and gene associations from… …edges with high posterior probability
are compared.
3
Chapter 2:
Bayesian probabilistic… …based regression likelihood,
low informative empirical priors and Bayesian model averaging…
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Patton, K. L. (2012). Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data. (Thesis). Wake Forest University. Retrieved from http://hdl.handle.net/10339/37257
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Patton, Kristopher Laurence. “Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data.” 2012. Thesis, Wake Forest University. Accessed March 05, 2021.
http://hdl.handle.net/10339/37257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Patton, Kristopher Laurence. “Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data.” 2012. Web. 05 Mar 2021.
Vancouver:
Patton KL. Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data. [Internet] [Thesis]. Wake Forest University; 2012. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/10339/37257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Patton KL. Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data. [Thesis]. Wake Forest University; 2012. Available from: http://hdl.handle.net/10339/37257
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Chicago
16.
Kaminski-Ozturk, Nicole.
A Bayesian Robust IRT Outlier Detection Model.
Degree: 2018, University of Illinois – Chicago
URL: http://hdl.handle.net/10027/23273
► In the context of psychometric practice, the parameter estimates of a standard item-response theory (IRT) model may become biased when item-response data, of persons’ individual…
(more)
▼ In the context of psychometric practice, the parameter estimates of a standard item-response theory (IRT) model may become biased when item-response data, of persons’ individual responses to test items, contain outliers relative to the model. Further, the manual removal of outliers can be a time-consuming and difficult task. Besides, removing outliers leads to data information loss in parameter estimation. To address these concerns, a
Bayesian IRT model that includes person and latent item-response outlier parameters, in addition to person ability and item parameters, is proposed and illustrated, and defined by item characteristic curves (ICCs) that are each specified by a robust, Student’s t-distribution function. The outlier parameters and the robust ICCs enable the model to automatically identify item-response outliers, and to make estimates of the person ability and item parameters more robust in the presence of outliers. Hence, under this IRT model, it is unnecessary to remove outliers from the data analysis.
The
Bayesian IRT model is illustrated through the analysis of two real-world, and two simulated datasets involving dichotomous- and polytomous-response items. Additionally, the model is applied to a simulated skewed dichotomously scored assessment to more closely understand how the model performs under realistic testing conditions.
Advisors/Committee Members: Karabatsos, George (advisor), Yin, Yue (committee member), Hedeker, Donald (committee member), Demirtas, Hakan (committee member), Maggin, Daniel (committee member), Karabatsos, George (chair).
Subjects/Keywords: Psychometrics; Bayesian
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kaminski-Ozturk, N. (2018). A Bayesian Robust IRT Outlier Detection Model. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/23273
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kaminski-Ozturk, Nicole. “A Bayesian Robust IRT Outlier Detection Model.” 2018. Thesis, University of Illinois – Chicago. Accessed March 05, 2021.
http://hdl.handle.net/10027/23273.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kaminski-Ozturk, Nicole. “A Bayesian Robust IRT Outlier Detection Model.” 2018. Web. 05 Mar 2021.
Vancouver:
Kaminski-Ozturk N. A Bayesian Robust IRT Outlier Detection Model. [Internet] [Thesis]. University of Illinois – Chicago; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/10027/23273.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kaminski-Ozturk N. A Bayesian Robust IRT Outlier Detection Model. [Thesis]. University of Illinois – Chicago; 2018. Available from: http://hdl.handle.net/10027/23273
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Louisiana State University
17.
Bhale, Ishan Singh.
Bayesian inference application to burglary detection.
Degree: MSCS, Computer Sciences, 2012, Louisiana State University
URL: etd-01222013-155000
;
https://digitalcommons.lsu.edu/gradschool_theses/2382
► Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic…
(more)
▼ Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic activities evident in various surveillance footages [61]. Surveillance videos can contain high level plans in the form of complex activities [61]. These complex activities are usually a combination of various articulated activities like breaking windshield, digging, and non-articulated activities like walking, running. We have developed a Bayesian framework for recognizing complex activities like burglary. This framework (belief network) is based on an expectation propagation algorithm [8] for approximate Bayesian inference. We provide experimental results showing the application of our framework for automatically detecting burglary from surveillance videos in real time.
Subjects/Keywords: Bayesian Inference
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Chicago ·
MLA ·
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CSE |
Export
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APA (6th Edition):
Bhale, I. S. (2012). Bayesian inference application to burglary detection. (Masters Thesis). Louisiana State University. Retrieved from etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382
Chicago Manual of Style (16th Edition):
Bhale, Ishan Singh. “Bayesian inference application to burglary detection.” 2012. Masters Thesis, Louisiana State University. Accessed March 05, 2021.
etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382.
MLA Handbook (7th Edition):
Bhale, Ishan Singh. “Bayesian inference application to burglary detection.” 2012. Web. 05 Mar 2021.
Vancouver:
Bhale IS. Bayesian inference application to burglary detection. [Internet] [Masters thesis]. Louisiana State University; 2012. [cited 2021 Mar 05].
Available from: etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382.
Council of Science Editors:
Bhale IS. Bayesian inference application to burglary detection. [Masters Thesis]. Louisiana State University; 2012. Available from: etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382

University of Oxford
18.
Vanetti, Paul.
Piecewise-deterministic Markov chain Monte Carlo.
Degree: PhD, 2019, University of Oxford
URL: http://ora.ox.ac.uk/objects/uuid:f5b05fdc-e461-4210-9dc3-0f0ef20de7a2
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820671
► Recent interest in a class of Markov chain Monte Carlo schemes based on continuous-time piecewise-deterministic Markov processes has led to several new and promising algorithmic…
(more)
▼ Recent interest in a class of Markov chain Monte Carlo schemes based on continuous-time piecewise-deterministic Markov processes has led to several new and promising algorithmic developments. Prominent examples include the zig-zag process and the bouncy particle sampler. We explore this class of algorithms, proposing extensions and drawing connections to existing literature. Two key aspects of a continuous-time piecewise-deterministic process are the flow and the event kernel. We discuss conditions on these sufficient to design a process targeting a distribution of interest. Based on these conditions, we show how the flow can be extended to Hamiltonian dynamics when exactly computable. For the event kernel, we demonstrate that a simple factorization of the invariant distribution subsumes all existing kernels and allows new kernels to be easily derived. Next, we explore the idea of piecewise-deterministic processes in discrete time. These algorithms have many close connections with pre-existing algorithms. The simultaneous simulation of multiple event rates may be connected to the discrete-time delayed acceptance algorithm. We demonstrate that the flow and event kernels used in the bouncy particle sampler bear strong resemblance to the reflective slice sampler. A class of discrete-time event kernels which we call here tunnelling algorithms also prove to be a special case of this framework. Applying this framework to the setting of large datasets with many observations, we describe how both the continuous-time and discrete-time algorithms can be adapted to this setting. We explore the use of approximations to bound the effects of individual observations. Methods of simulating from discrete distributions based on Poisson processes are used to efficiently subsample the data. Finally, we show how these methods can be used to extend and improve upon the existing firefly Monte Carlo algorithm. A second application is found in the setting where interaction between parameters is limited and forms a sparse graph, for which the continuous-time local bouncy particle sampler algorithm has been developed. A discrete-time extension mimicking the local properties of this algorithm is proposed, unveiling a close connection to the random cluster algorithm of Swendsen-Wang.
Subjects/Keywords: Bayesian statistics
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Vanetti, P. (2019). Piecewise-deterministic Markov chain Monte Carlo. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:f5b05fdc-e461-4210-9dc3-0f0ef20de7a2 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820671
Chicago Manual of Style (16th Edition):
Vanetti, Paul. “Piecewise-deterministic Markov chain Monte Carlo.” 2019. Doctoral Dissertation, University of Oxford. Accessed March 05, 2021.
http://ora.ox.ac.uk/objects/uuid:f5b05fdc-e461-4210-9dc3-0f0ef20de7a2 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820671.
MLA Handbook (7th Edition):
Vanetti, Paul. “Piecewise-deterministic Markov chain Monte Carlo.” 2019. Web. 05 Mar 2021.
Vancouver:
Vanetti P. Piecewise-deterministic Markov chain Monte Carlo. [Internet] [Doctoral dissertation]. University of Oxford; 2019. [cited 2021 Mar 05].
Available from: http://ora.ox.ac.uk/objects/uuid:f5b05fdc-e461-4210-9dc3-0f0ef20de7a2 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820671.
Council of Science Editors:
Vanetti P. Piecewise-deterministic Markov chain Monte Carlo. [Doctoral Dissertation]. University of Oxford; 2019. Available from: http://ora.ox.ac.uk/objects/uuid:f5b05fdc-e461-4210-9dc3-0f0ef20de7a2 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820671

University of Utah
19.
Rai, Piyush.
Learning latent structures via bayesian nonparametrics: new models and efficient inference.
Degree: PhD, Computer Science, 2013, University of Utah
URL: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3460/rec/1472
► Latent structures play a vital role in many data analysis tasks. By providing compactyet expressive representations, such structures can offer useful insights into the complexand…
(more)
▼ Latent structures play a vital role in many data analysis tasks. By providing compactyet expressive representations, such structures can offer useful insights into the complexand high-dimensional datasets encountered in domains such as computational biology,computer vision, natural language processing, etc. Specifying the right complexity ofthese latent structures for a given problem is an important modeling decision. Insteadof using models with an a priori fixed complexity, it is desirable to have models that canadapt their complexity as the data warrant. Nonparametric Bayesian models are motivatedprecisely based on this desideratum by offering a flexible modeling paradigm for datawithout limiting the model-complexity a priori. The flexibility comes from the model’sability to adjust its complexity adaptively with data.This dissertation is about nonparametric Bayesian learning of two specific types of latentstructures: (1) low-dimensional latent features underlying high-dimensional observeddata where the latent features could exhibit interdependencies, and (2) latent task structuresthat capture how a set of learning tasks relate with each other, a notion critical in theparadigm of Multitask Learning where the goal is to solve multiple learning tasks jointlyin order to borrow information across similar tasks.Another focus of this dissertation is on designing efficient approximate inference algorithmsfor nonparametric Bayesian models. Specifically, for the nonparametric Bayesianlatent feature model where the goal is to infer the binary-valued latent feature assignmentmatrix for a given set of observations, the dissertation proposes two approximate inferencemethods. The first one is a search-based algorithm to find the maximum-a-posteriori(MAP) solution for the latent feature assignment matrix. The second one is a sequentialMonte-Carlo-based approximate inference algorithm that allows processing the data oneexample-at-a-time while being space-efficient in terms of the storage required to representthe posterior distribution of the latent feature assignment matrix.
Subjects/Keywords: Bayesian learning; Bayesian nonparametrics; Machine learning
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rai, P. (2013). Learning latent structures via bayesian nonparametrics: new models and efficient inference. (Doctoral Dissertation). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3460/rec/1472
Chicago Manual of Style (16th Edition):
Rai, Piyush. “Learning latent structures via bayesian nonparametrics: new models and efficient inference.” 2013. Doctoral Dissertation, University of Utah. Accessed March 05, 2021.
http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3460/rec/1472.
MLA Handbook (7th Edition):
Rai, Piyush. “Learning latent structures via bayesian nonparametrics: new models and efficient inference.” 2013. Web. 05 Mar 2021.
Vancouver:
Rai P. Learning latent structures via bayesian nonparametrics: new models and efficient inference. [Internet] [Doctoral dissertation]. University of Utah; 2013. [cited 2021 Mar 05].
Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3460/rec/1472.
Council of Science Editors:
Rai P. Learning latent structures via bayesian nonparametrics: new models and efficient inference. [Doctoral Dissertation]. University of Utah; 2013. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3460/rec/1472

Princeton University
20.
Shiraito, Yuki.
Essays in Political Methodology
.
Degree: PhD, 2017, Princeton University
URL: http://arks.princeton.edu/ark:/88435/dsp01wd375z938
► This collection of essays studies Bayesian statistical models for data analysis in political science. The first chapter proposes a nonparametric Bayesian approach that uncovers heterogeneous…
(more)
▼ This collection of essays studies
Bayesian statistical models for data analysis in political science.
The first chapter proposes a nonparametric
Bayesian approach that uncovers heterogeneous treatment effects even when moderators are unobserved. The method employs a Dirichlet process mixture model to estimate the distribution of treatment effects, and it is applicable to any setting in which regression models are used for causal inference. An application to a study on resource curse also shows that the method finds the subset of observations for which the monotonicity assumption is likely to hold.
The second chapter proposes a new topic model for analyzing plagiarism. Text is modeled as a mixture of words copied from the plagiarized source and words drawn from a new distribution, and the model provides estimated probabilities of plagiarism for each word. An application to the corpus of preferential trade arrangements shows the utility of the model by describing how the likelihood of plagiarism varies across topics.
The third chapter develops an n-gram based topic model for analyzing citations and text. It introduces a latent variable indicating whether each token is in the positive or negative mode and assumes dependence between the mode of citation and its previous terms. An application to the data set of the WTO dispute settlement mechanism shows that the conclusion of an existing study using the same data may hold only in some issues.
The fourth chapter (coauthored with Gabriel Lopez-Moctezuma and Devin Incerti) reexamines resource curse using the
Bayesian dynamic linear model (DLM). The DLM models both temporal dependence in the data and the evolution of parameters. The results show that there is a negative relationship between oil income and the level of democracy only after the 1970s, which coincides with the Arab oil embargo and the nationalization of the oil industry in major oil-exporting countries.
Advisors/Committee Members: Imai, Kosuke (advisor).
Subjects/Keywords: Bayesian nonparametrics;
Bayesian statistics;
Methodology;
Text analysis
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shiraito, Y. (2017). Essays in Political Methodology
. (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01wd375z938
Chicago Manual of Style (16th Edition):
Shiraito, Yuki. “Essays in Political Methodology
.” 2017. Doctoral Dissertation, Princeton University. Accessed March 05, 2021.
http://arks.princeton.edu/ark:/88435/dsp01wd375z938.
MLA Handbook (7th Edition):
Shiraito, Yuki. “Essays in Political Methodology
.” 2017. Web. 05 Mar 2021.
Vancouver:
Shiraito Y. Essays in Political Methodology
. [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2021 Mar 05].
Available from: http://arks.princeton.edu/ark:/88435/dsp01wd375z938.
Council of Science Editors:
Shiraito Y. Essays in Political Methodology
. [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01wd375z938

University of Georgia
21.
Shackelford, Robert Hill.
Using Bayesian model averaging to improve hurricane track forecasts.
Degree: 2018, University of Georgia
URL: http://hdl.handle.net/10724/37504
► I study whether Bayesian composite forecasting can produce improved track forecasts for hurricanes. Using data on hurricanes back to 2005, the first step is to…
(more)
▼ I study whether Bayesian composite forecasting can produce improved track forecasts for hurricanes. Using data on hurricanes back to 2005, the first step is to find a set of storms most similar to the one to have its track forecast. Then,
the performance of ten hurricane forecasting models on those similar storms is used to calculate the weights that will be placed on each of these models. These weights are used to form a Bayesian composite forecast of the track for the hurricane of
interest, rather than the currently, more standard, simple average utilized by the National Hurricane Center (NHC). On a small selection of recent hurricanes, the performance of the Bayesian composite forecast tracks are compared to the individual model
forecasts and the NHC official forecasts. In most of our cases, the Bayesian composite forecast is more accurate than the NHC forecast.
Subjects/Keywords: Bayesian; Hurricane Forecasting; Bayesian Model Averaging; Tracking
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shackelford, R. H. (2018). Using Bayesian model averaging to improve hurricane track forecasts. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37504
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shackelford, Robert Hill. “Using Bayesian model averaging to improve hurricane track forecasts.” 2018. Thesis, University of Georgia. Accessed March 05, 2021.
http://hdl.handle.net/10724/37504.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shackelford, Robert Hill. “Using Bayesian model averaging to improve hurricane track forecasts.” 2018. Web. 05 Mar 2021.
Vancouver:
Shackelford RH. Using Bayesian model averaging to improve hurricane track forecasts. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/10724/37504.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shackelford RH. Using Bayesian model averaging to improve hurricane track forecasts. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37504
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New South Wales
22.
Tang, Yating.
Prior information and multi-objective analysis in Bayesian ecohydrological modeling.
Degree: Civil & Environmental Engineering, 2017, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/60071
;
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true
► This thesis presents a Bayesian multi-objective calibration approach associated with uncertainty analysis in ecohydrological modeling. This work focuses on three main objectives: (1) the development…
(more)
▼ This thesis presents a
Bayesian multi-objective calibration approach associated with uncertainty analysis in ecohydrological modeling. This work focuses on three main objectives: (1) the development and extension of
Bayesian inference for hydrological models focused on the evaluation of the prior distributions; (2) the application of a formal
Bayesian multi-objective approach to an ecohydrological model; and (3) the analysis of observation uncertainties in ecohydrological modelling, which is subdivided to the analysis of input uncertainty and output uncertainty in a
Bayesian multi-objective framework. The first part of work focuses on the investigation of the importance of prior information in
Bayesian inference. A toolkit is introduced to evaluate the impact of prior distributions on the posterior distribution in a conceptual rainfall-runoff model. In the study, the Kullback-Leibler divergence is used to quantify the impact of different priors, and the prior information elasticity is introduced to evaluate the importance of prior distributions for each model parameter. Results show that the prior distribution can dramatically affect the posterior distributions for insensitive model parameters, and it is suggested that meaningful prior distributions need to be defined appropriately for model parameters. Next, this thesis focuses on the application of a
Bayesian multi-objective approach in ecohydrological modeling. Ecohydrological models are more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analyses are essential for ecohydrological modeling. A multi-objective
Bayesian calibration method is introduced. Specified prior distributions for error parameters for each of the objectives are defined which represent the weight of each objective based on the information from a traditional Pareto-based multi-objective optimization approach. Results show a better estimation of both of the objectives using this approach.Finally, the impact of observation errors on model predictions is addressed by investigating: (a) the effects of precipitation error (input error) in multi-objective analysis where different precipitation error descriptions are compared; and (b) the effects of satellite errors (output error) in specifying vegetation simulations where LAI observation error is defined using data quality information about the satellite derived product. Results suggest a detailed description of observation errors needs to be included in
Bayesian ecohydrologcial modeling.
Advisors/Committee Members: Marshall, Lucy, Civil & Environmental Engineering, Faculty of Engineering, UNSW, Sharma, Ashish, Civil & Environmental Engineering, Faculty of Engineering, UNSW.
Subjects/Keywords: ecohydrological model; Bayesian ecohydrological modeling; Bayesian
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Tang, Y. (2017). Prior information and multi-objective analysis in Bayesian ecohydrological modeling. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true
Chicago Manual of Style (16th Edition):
Tang, Yating. “Prior information and multi-objective analysis in Bayesian ecohydrological modeling.” 2017. Doctoral Dissertation, University of New South Wales. Accessed March 05, 2021.
http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true.
MLA Handbook (7th Edition):
Tang, Yating. “Prior information and multi-objective analysis in Bayesian ecohydrological modeling.” 2017. Web. 05 Mar 2021.
Vancouver:
Tang Y. Prior information and multi-objective analysis in Bayesian ecohydrological modeling. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2021 Mar 05].
Available from: http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true.
Council of Science Editors:
Tang Y. Prior information and multi-objective analysis in Bayesian ecohydrological modeling. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/60071 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51327/SOURCE02?view=true

Tulane University
23.
Aleksandra Gorzycka.
Bayesian Updating and Statistical Inference for Beta-binomial Models.
Degree: 2018, Tulane University
URL: https://digitallibrary.tulane.edu/islandora/object/tulane:81679
► The Beta-binomial distribution is often employed as a model for count data in cases where the observed dispersion is greater than would be expected for…
(more)
▼ The Beta-binomial distribution is often employed as a model for count data in cases where the observed dispersion is greater than would be expected for the standard binomial distribution. Parameter estimation in this setting is typically performed using a Bayesian approach, which requires specifying appropriate prior distributions for parameters. In the context of many applications, incorporating estimates from previous analyses can offer advantages over naive or diffuse priors. An example of this is in the food security setting, where baseline consumption surveys can inform parameter estimation in crisis situations during which data must be collected hastily on smaller samples of individuals. We have developed an approach for Bayesian updating in the beta-binomial model that incorporates adjustable prior weights and enables inference using a bivariate normal approximation for the mode of the posterior distribution. Our methods, which are implemented in the R programming environment, include tools for the estimation of statistical power to detect changes in parameter values.
1
Aleksandra Gorzycka
Advisors/Committee Members: (author), Michelle Lacey (Thesis advisor), (Thesis advisor), School of Science & Engineering Mathematics (Degree granting institution), NULL (Degree granting institution).
Subjects/Keywords: Statistics; Bayesian Analysis
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gorzycka, A. (2018). Bayesian Updating and Statistical Inference for Beta-binomial Models. (Thesis). Tulane University. Retrieved from https://digitallibrary.tulane.edu/islandora/object/tulane:81679
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Gorzycka, Aleksandra. “Bayesian Updating and Statistical Inference for Beta-binomial Models.” 2018. Thesis, Tulane University. Accessed March 05, 2021.
https://digitallibrary.tulane.edu/islandora/object/tulane:81679.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Gorzycka, Aleksandra. “Bayesian Updating and Statistical Inference for Beta-binomial Models.” 2018. Web. 05 Mar 2021.
Vancouver:
Gorzycka A. Bayesian Updating and Statistical Inference for Beta-binomial Models. [Internet] [Thesis]. Tulane University; 2018. [cited 2021 Mar 05].
Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:81679.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Gorzycka A. Bayesian Updating and Statistical Inference for Beta-binomial Models. [Thesis]. Tulane University; 2018. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:81679
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University
24.
Moridis, Nefeli G.
A Production Characterization of the Eagle Ford Shale, Texas - A Bayesian Analysis Approach.
Degree: MS, Petroleum Engineering, 2015, Texas A&M University
URL: http://hdl.handle.net/1969.1/155011
► We begin this research by asking "can we better estimate reserves in unconventional reservoirs using Bayes' theorem?" To attempt to answer this question, we obtained…
(more)
▼ We begin this research by asking "can we better estimate reserves in unconventional reservoirs using Bayes' theorem?" To attempt to answer this question, we obtained data for 68 wells in the Greater Core of the Eagle Ford Shale, Texas. As process, we eliminated the wells that did not have enough data, that did not show a production decline and/or wells that had too much data noise (this left us with 8 wells for analysis). We next performed decline curve analysis (DCA) using the Modified Hyperbolic (MH) and Power-Law Exponential (PLE) models (the two most common DCA models), consisting in user-guided analysis software. Then, the
Bayesian paradigm was implemented to calibrate the same two models on the same set of wells.
The primary focus of the research was the implementation of the
Bayesian paradigm on the 8 well data set. We first performed a "best fit" parameter estimation using least squares optimization, which provided an optimized set of parameters for the two decline curve models. This was followed by using the Markov Chain Monte Carlo (MCMC) integration of the
Bayesian posterior function for each model, which provided a full probabilistic description of its parameters. This allowed for the simulation of a number of likely realizations of the decline curves, from which first order statistics were computed to provide a confidence metric on the calibration of each model as applied to the production data of each well.
Results showed variation on the calibration of the MH and PLE models. The forward models (MH and PLE) either over- or underestimate the reserves compared with the
Bayesian calibrations, proving that the
Bayesian paradigm was able to capture a more accurate trend of the data and thus able to determine more accurate estimates of reserves. In industry, the same decline curve models are used for unconventional wells as for conventional wells, even though we know that the same models may not apply. Based on the proposed results, we believe that
Bayesian inference yields more accurate estimates of reserves for unconventional reservoirs than deterministic DCA methods. Moreover, it provides a measure of confidence on the prediction of production as as function of varying data and varying decline curve models.
Advisors/Committee Members: Blasingame, Thomas A (advisor), Medina-Cetina, Zenon (advisor), Ayers, Water (committee member).
Subjects/Keywords: Production Characterization; Bayesian
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Moridis, N. G. (2015). A Production Characterization of the Eagle Ford Shale, Texas - A Bayesian Analysis Approach. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/155011
Chicago Manual of Style (16th Edition):
Moridis, Nefeli G. “A Production Characterization of the Eagle Ford Shale, Texas - A Bayesian Analysis Approach.” 2015. Masters Thesis, Texas A&M University. Accessed March 05, 2021.
http://hdl.handle.net/1969.1/155011.
MLA Handbook (7th Edition):
Moridis, Nefeli G. “A Production Characterization of the Eagle Ford Shale, Texas - A Bayesian Analysis Approach.” 2015. Web. 05 Mar 2021.
Vancouver:
Moridis NG. A Production Characterization of the Eagle Ford Shale, Texas - A Bayesian Analysis Approach. [Internet] [Masters thesis]. Texas A&M University; 2015. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1969.1/155011.
Council of Science Editors:
Moridis NG. A Production Characterization of the Eagle Ford Shale, Texas - A Bayesian Analysis Approach. [Masters Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/155011

University of Newcastle
25.
Tran, Khoa T.
Computational solutions for Bayesian inference in dynamical systems.
Degree: MPhil, 2016, University of Newcastle
URL: http://hdl.handle.net/1959.13/1322481
► Masters Research - Master of Philosophy (MPhil)
This thesis proposes Bayesian inference as a feasible and possibly, under some circumstances, preferable alternative to mainstream approaches…
(more)
▼ Masters Research - Master of Philosophy (MPhil)
This thesis proposes Bayesian inference as a feasible and possibly, under some circumstances, preferable alternative to mainstream approaches in dynamic system identification such as prediction error and maximum likelihood methods. The advantages of the Bayesian approach are demonstrated through empirical study of linear time invariant system identification with short and noisy data record. Empirical evidence for the minimum mean square error property of the Bayesian estimator under practical finite data length scenarios is presented. Multiple methods for approximating the high dimensional integration associated with Bayesian inference are also thoroughly analysed. Specifically, the state–of–the–art in computational design is reviewed through the analysis of two families of Markov Chain Monte Carlo algorithms, among other Monte Carlo and conventional numerical integration methods. Many practical combinations and adaptations of these well researched Markov Chain Monte Carlo algorithms are also presented. Empirical evidence of geometric convergence rate O(1/M) of the square error in Markov Chain Monte Carlo integration is also given for dynamic system with up to 12 parameters
Advisors/Committee Members: University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science.
Subjects/Keywords: MCMC; Bayesian computation
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APA (6th Edition):
Tran, K. T. (2016). Computational solutions for Bayesian inference in dynamical systems. (Masters Thesis). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1322481
Chicago Manual of Style (16th Edition):
Tran, Khoa T. “Computational solutions for Bayesian inference in dynamical systems.” 2016. Masters Thesis, University of Newcastle. Accessed March 05, 2021.
http://hdl.handle.net/1959.13/1322481.
MLA Handbook (7th Edition):
Tran, Khoa T. “Computational solutions for Bayesian inference in dynamical systems.” 2016. Web. 05 Mar 2021.
Vancouver:
Tran KT. Computational solutions for Bayesian inference in dynamical systems. [Internet] [Masters thesis]. University of Newcastle; 2016. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/1959.13/1322481.
Council of Science Editors:
Tran KT. Computational solutions for Bayesian inference in dynamical systems. [Masters Thesis]. University of Newcastle; 2016. Available from: http://hdl.handle.net/1959.13/1322481

Queensland University of Technology
26.
Drovandi, Christopher Colin.
Bayesian algorithms with applications.
Degree: 2012, Queensland University of Technology
URL: https://eprints.qut.edu.au/57317/
► Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be…
(more)
▼ Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications.
For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference.
Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC.
The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty.
Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away.
When…
Subjects/Keywords: Bayesian algorithms; applications
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Drovandi, C. C. (2012). Bayesian algorithms with applications. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/57317/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Drovandi, Christopher Colin. “Bayesian algorithms with applications.” 2012. Thesis, Queensland University of Technology. Accessed March 05, 2021.
https://eprints.qut.edu.au/57317/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Drovandi, Christopher Colin. “Bayesian algorithms with applications.” 2012. Web. 05 Mar 2021.
Vancouver:
Drovandi CC. Bayesian algorithms with applications. [Internet] [Thesis]. Queensland University of Technology; 2012. [cited 2021 Mar 05].
Available from: https://eprints.qut.edu.au/57317/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Drovandi CC. Bayesian algorithms with applications. [Thesis]. Queensland University of Technology; 2012. Available from: https://eprints.qut.edu.au/57317/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Queensland University of Technology
27.
Lee, Jeong Eun.
Bayesian hybrid algorithms and models : implementation and associated issues.
Degree: 2010, Queensland University of Technology
URL: https://eprints.qut.edu.au/33151/
► This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’…
(more)
▼ This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of…
Subjects/Keywords: Bayesian hybrid algorithms
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lee, J. E. (2010). Bayesian hybrid algorithms and models : implementation and associated issues. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/33151/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lee, Jeong Eun. “Bayesian hybrid algorithms and models : implementation and associated issues.” 2010. Thesis, Queensland University of Technology. Accessed March 05, 2021.
https://eprints.qut.edu.au/33151/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lee, Jeong Eun. “Bayesian hybrid algorithms and models : implementation and associated issues.” 2010. Web. 05 Mar 2021.
Vancouver:
Lee JE. Bayesian hybrid algorithms and models : implementation and associated issues. [Internet] [Thesis]. Queensland University of Technology; 2010. [cited 2021 Mar 05].
Available from: https://eprints.qut.edu.au/33151/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lee JE. Bayesian hybrid algorithms and models : implementation and associated issues. [Thesis]. Queensland University of Technology; 2010. Available from: https://eprints.qut.edu.au/33151/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Baylor University
28.
Cheng, Joyce H., 1986-.
Bayesian methods for hurdle models.
Degree: PhD, Baylor University. Dept. of Statistical Sciences., 2015, Baylor University
URL: http://hdl.handle.net/2104/9297
► Hurdle models are often presented as an alternative to zero-inflated models for count data with excess zeros. They consist of two parts: a binary model…
(more)
▼ Hurdle models are often presented as an alternative to zero-inflated models for count data with excess zeros. They consist of two parts: a binary model indicating a positive response (the “hurdle”) and a zero-truncated count model. One or both parts of the model can depend on covariates, which may or may not coincide. In this dissertation, we explore the
Bayesian approach to these models in detail, focusing on prior structures. Many of the
Bayesian hurdle models encountered in the literature fail to incorporate expert opinion into the prior structure. We consider how prior information can be elicited from experts and incorporated into the prior structure of a hurdle model with shared covariates through the use of conditional means priors. More specifically, we propose a prior structure that assumes an inherent functional relationship between the two parts of the model. Through simulations, we explore the potential gains, as well as the shortcomings, of the approach. We also consider a simulation algorithm for
Bayesian sample size determination for such models. We illustrate the use of the new methods on data from a hypothetical sleep disorder study.
Advisors/Committee Members: Kahle, David J. (advisor), Seaman, John Weldon, 1956- (advisor).
Subjects/Keywords: Hurdle model; Bayesian
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Cheng, Joyce H., 1. (2015). Bayesian methods for hurdle models. (Doctoral Dissertation). Baylor University. Retrieved from http://hdl.handle.net/2104/9297
Chicago Manual of Style (16th Edition):
Cheng, Joyce H., 1986-. “Bayesian methods for hurdle models.” 2015. Doctoral Dissertation, Baylor University. Accessed March 05, 2021.
http://hdl.handle.net/2104/9297.
MLA Handbook (7th Edition):
Cheng, Joyce H., 1986-. “Bayesian methods for hurdle models.” 2015. Web. 05 Mar 2021.
Vancouver:
Cheng, Joyce H. 1. Bayesian methods for hurdle models. [Internet] [Doctoral dissertation]. Baylor University; 2015. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/2104/9297.
Council of Science Editors:
Cheng, Joyce H. 1. Bayesian methods for hurdle models. [Doctoral Dissertation]. Baylor University; 2015. Available from: http://hdl.handle.net/2104/9297

University of Cape Town
29.
Nickless, Alecia.
Regional CO₂ flux estimates for South Africa through inverse modelling.
Degree: PhD, Statistical Sciences, 2018, University of Cape Town
URL: http://hdl.handle.net/11427/29703
► Bayesian inverse modelling provides a top-down technique of verifying emissions and uptake of carbon dioxide (CO₂) from both natural and anthropogenic sources. It relies on…
(more)
▼ Bayesian inverse modelling provides a top-down technique of verifying emissions and uptake of carbon dioxide (CO₂) from both natural and anthropogenic sources. It relies on accurate measurements of CO₂ concentrations at appropriately placed sites and "best-guess" initial estimates of the biogenic and anthropogenic emissions, together with uncertainty estimates. The
Bayesian framework improves current estimates of CO₂ fluxes based on independent measurements of CO₂ concentrations while being constrained by the initial estimates of these fluxes. Monitoring, reporting and verification (MRV) is critical for establishing whether emission reducing activities to mitigate the effects of climate change are being effective, and the
Bayesian inverse modelling approach of correcting CO₂ flux estimates provides one of the tools regulators and researchers can use to refine these emission estimates. South Africa is known to be the largest emitter of CO₂ on the African continent. The first major objective of this research project was to carry out such an optimal network design for South Africa. This study used fossil fuel emission estimates from a satellite product based on observations of night-time lights and locations of power stations (Fossil Fuel Data Assimilations System (FFDAS)), and biogenic productivity estimates from a carbon assessment carried out for South Africa to provide the initial CO₂ flux estimates and their uncertainties. Sensitivity analyses considered changes to the covariance matrix and spatial scale of the inversion, as well as different optimisation algorithms, to assess the impact of these specifications on the optimal network solution. This question was addressed in Chapters 2 and 3. The second major objective of this project was to use the
Bayesian inverse modelling approach to obtain estimates of CO₂ fluxes over Cape Town and surrounding area. I collected measurements of atmospheric CO₂ concentrations from March 2012 until July 2013 at Robben Island and Hangklip lighthouses. CABLE (Community Atmosphere Biosphere Land Exchange), a land-atmosphere exchange model, provided the biogenic estimates of CO₂ fluxes and their uncertainties. Fossil fuel estimates and uncertainties were obtained by means of an inventory analysis for Cape Town. As an inventory analysis was not available for Cape Town, this exercise formed an additional objective of the project, presented in Chapter 4. A spatially and temporally explicit, high resolution surface of fossil fuel emission estimates was derived from road vehicle, aviation and shipping vessel count data, population census data, and industrial fuel use statistics, making use of well-established emission factors. The city-scale inversion for Cape Town solved for weekly fluxes of CO₂ emissions on a 1 km × 1 km grid, keeping fossil fuel and biogenic emissions as separate sources. I present these results for the Cape Town inversion under the proposed best available configuration of the
Bayesian inversion framework in Chapter 5. Due to the large number of CO₂ sources at this…
Advisors/Committee Members: Rayner, Peter (advisor), Scholes, Bob (advisor), Erni, Birgit (advisor), Underhill, Leslie G (advisor).
Subjects/Keywords: Bayesian inverse modelling
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Nickless, A. (2018). Regional CO₂ flux estimates for South Africa through inverse modelling. (Doctoral Dissertation). University of Cape Town. Retrieved from http://hdl.handle.net/11427/29703
Chicago Manual of Style (16th Edition):
Nickless, Alecia. “Regional CO₂ flux estimates for South Africa through inverse modelling.” 2018. Doctoral Dissertation, University of Cape Town. Accessed March 05, 2021.
http://hdl.handle.net/11427/29703.
MLA Handbook (7th Edition):
Nickless, Alecia. “Regional CO₂ flux estimates for South Africa through inverse modelling.” 2018. Web. 05 Mar 2021.
Vancouver:
Nickless A. Regional CO₂ flux estimates for South Africa through inverse modelling. [Internet] [Doctoral dissertation]. University of Cape Town; 2018. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/11427/29703.
Council of Science Editors:
Nickless A. Regional CO₂ flux estimates for South Africa through inverse modelling. [Doctoral Dissertation]. University of Cape Town; 2018. Available from: http://hdl.handle.net/11427/29703

Victoria University of Wellington
30.
Large, Kathleen.
Population changes in rattail species on the Chatham Rise.
Degree: 2014, Victoria University of Wellington
URL: http://hdl.handle.net/10063/3446
► The aim of this project was to conduct a stock assessment to determine the population dynamic characteristics of rattail species taken as bycatch in the…
(more)
▼ The aim of this project was to conduct a stock assessment to determine the population dynamic characteristics of rattail species taken as bycatch in the hoki, hake and ling fishery on the Chatham Rise. No quantitative assessment of the current size of rattail populations , and how these may have changed over time, has been carried out before. There is interest in the need to quantify the impact of commercial fishing on the rattail populations, as rattails (Macrouridae family) are considered to be an ecologically important species complex in the deep ocean, and there may be the potential for the development of a commercial fishery based on their value as processed fishmeal. The minimum data required for a stock assessment are an abundance index and a catch history. Abundance indices are available for over 20 species of rattail produced from scientific surveys conducted annually on the Chatham Rise since 1992. Catch histories for individual rattail species in the same area are not available. A method was developed to reconstruct commercial catches of rattails from commercial effort data and survey catch and effort data. A surplus production model was fitted to the reconstructed catch data and survey abundance indices, using maximum likelihood and
Bayesian methods to estimate model parameters and uncertainty. A surplus production model has two components: an observation model for abundance indices and a process model for population dynamics. Maximum likelihood estimation was applied to a model that specified errors for the observations only, and this produced estimates that had wide confidence intervals. A
Bayesian approach was then taken to fit a statespace version of the model that incorporates errors associated with the observation and process models. While the
Bayesian method produced more plausible parameter estimates (in comparison to the maximum likelihood method) and parameter uncertainty was reduced, our analysis indicated the posterior estimates were highly sensitive to the specification of different priors. There may be several reasons for these results, including: the small number of observations, lack of contrast in the data and mis-specification of the model. Meaningful estimates of the absolute size of rattail populations are not possible with these results, where estimates can vary by orders of magnitude depending on prior specification. This implies that more work needs to be done to develop more effective methods that can be used to help inform decisions regarding the management of these fish populations. Improving data collection, investigating informative priors and extending/respecifying the model are considered worthwhile avenues of future work to improve stock assessments of rattails.
Advisors/Committee Members: Sibanda, Nokuthaba, Arnold, Richard.
Subjects/Keywords: Fisheries; Model; Bayesian
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Large, K. (2014). Population changes in rattail species on the Chatham Rise. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/3446
Chicago Manual of Style (16th Edition):
Large, Kathleen. “Population changes in rattail species on the Chatham Rise.” 2014. Masters Thesis, Victoria University of Wellington. Accessed March 05, 2021.
http://hdl.handle.net/10063/3446.
MLA Handbook (7th Edition):
Large, Kathleen. “Population changes in rattail species on the Chatham Rise.” 2014. Web. 05 Mar 2021.
Vancouver:
Large K. Population changes in rattail species on the Chatham Rise. [Internet] [Masters thesis]. Victoria University of Wellington; 2014. [cited 2021 Mar 05].
Available from: http://hdl.handle.net/10063/3446.
Council of Science Editors:
Large K. Population changes in rattail species on the Chatham Rise. [Masters Thesis]. Victoria University of Wellington; 2014. Available from: http://hdl.handle.net/10063/3446
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