subject:(bayesian)

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)

Subjects/Keywords: bayesian

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APA (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Bayesian

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Bayesian inference

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: bayesian inference

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Bayesian

…common to use an autologistic model to predict presence or absence within a Bayesian…

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APA (6^th 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 (16^th 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 (7^th 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)

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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Psychometrics; Bayesian

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APA (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Bayesian Inference

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Bayesian; Hurricane Forecasting; Bayesian Model Averaging; Tracking

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Statistics; Bayesian Analysis

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: MCMC; Bayesian computation

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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)

Subjects/Keywords: Hurdle model; Bayesian

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APA (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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 (6^th 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 (16^th 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 (7^th 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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