subject:(Bayesian Inference)

1. 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 September 22, 2019. 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. 22 Sep 2019.

Vancouver:

Doraiswamy S. Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach. [Internet] [Thesis]. Texas Digital Library; 2013. [cited 2019 Sep 22]. 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

Louisiana State University

2. 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 September 22, 2019. 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. 22 Sep 2019.

Vancouver:

Bhale IS. Bayesian inference application to burglary detection. [Internet] [Masters thesis]. Louisiana State University; 2012. [cited 2019 Sep 22]. 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

3. 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 September 22, 2019. 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. 22 Sep 2019.

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 2019 Sep 22]. 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/

4. 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 September 22, 2019. 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. 22 Sep 2019.

Vancouver:

Lin L. Bayesian Inference and High-D space Characterization with application in Paleoclimatology and Biology. [Internet] [Doctoral dissertation]. Brown University; 2012. [cited 2019 Sep 22]. 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/

Texas A&M University

5. Das, Roneet. Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference.

Degree: MS, Civil Engineering, 2016, Texas A&M University

URL: http://hdl.handle.net/1969.1/157070

► Estimates of probability of slope failure based on Monte-Carlo methods depend upon the state of evidence on the slope stability model parameters. The Bayesian framework… (more)

▼ Estimates of probability of slope failure based on Monte-Carlo methods depend upon the state of evidence on the slope stability model parameters. The Bayesian framework illustrated in this paper to estimate the probability of failure against submarine landslide incorporates experimental data (undrained shear strength) with the initial state of evidence on the model parameters to achieve more certain and accurate model predictions and estimates of probability of failure. The objective of this research was to determine the probability of failure of a submarine slope due to static loading conditions for a given state of evidence (e.g. soil data, slope stability model and expert beliefs). A physics-based forward model (infinite slope) was adopted to evaluate the probability of failure against sliding. The geotechnical and geometric parameters (unit weight of the slice, thickness, pseudo-static seismic coefficient and slope angle) of the proposed model were regarded as random variables. The initial state of evidence and level of uncertainty associated with the proposed model parameters were presented as prior probability distribution functions (log-normal distribution). The Bayesian framework was adopted to calibrate the proposed model with synthetically generated experimental observations representing different in-situ undrained shear strength conditions. Model predictions on the mobilized shear strength when sampled from posterior distributions of the model parameters showed greater certainty and accuracy with respect to the Monte-Carlo forward model simulations based on the prior distributions. Results showed significant changes in the landslide probability with the increase in amount of data for two scenarios used for model calibration, while indicating the correlation structure changes among the model parameters. This allowed to estimating the sampling scenarios and their corresponding confidence gains prior to a field investigation. Advisors/Committee Members: Medina-Cetina, Zenon (advisor), Aubeny, Charles (committee member), Kim, Jihoon (committee member).

Subjects/Keywords: Slope Stability; Bayesian Inference

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APA (6^th Edition):

Das, R. (2016). Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/157070

Chicago Manual of Style (16^th Edition):

Das, Roneet. “Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference.” 2016. Masters Thesis, Texas A&M University. Accessed September 22, 2019. http://hdl.handle.net/1969.1/157070.

MLA Handbook (7^th Edition):

Das, Roneet. “Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference.” 2016. Web. 22 Sep 2019.

Vancouver:

Das R. Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference. [Internet] [Masters thesis]. Texas A&M University; 2016. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/1969.1/157070.

Council of Science Editors:

Das R. Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference. [Masters Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/157070

University of Sydney

6. Abeywardana, Sachinthaka. Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions .

Degree: 2015, University of Sydney

URL: http://hdl.handle.net/2123/16504

► Most real world data are skewed, contain more than the set of real numbers, and have higher probabilities of extreme events occurring compared to a… (more)

▼ Most real world data are skewed, contain more than the set of real numbers, and have higher probabilities of extreme events occurring compared to a normal distribution. In this thesis we explore two non-Gaussian distributions, the Generalised Hyperbolic Distribution (GHD) and, the von-Mises Fisher (vMF) Distribution. These distributions are studied in the context of 1) Regression in heavy tailed data, 2) Quantifying variance of functions with reference to finding relevant quantiles and, 3) Clustering data that lie on the surface of the sphere. Firstly, we extend Gaussian Processes (GPs) and use the Genralised Hyperbolic Processes as a prior on functions instead. This prior is more flexible than GPs and is especially able to model data that has high kurtosis. The method is based on placing a Generalised Inverse Gaussian prior over the signal variance, which yields a scalar mixture of GPs. We show how to perform inference efficiently for the predictive mean and variance, and use a variational EM method for learning. Secondly, the skewed extension of the GHD is studied with respect to quantile regression. An underlying GP prior on the quantile function is used to make the inference non-parametric, while the skewed GHD is used as the data likelihood. The skewed GHD has a single parameter alpha which states the required quantile. Similar variational methods as the first contribution is used to perform inference. Finally, vMF distributions are introduced in order to cluster spherical data. In the two previous contributions continuous scalar mixtures of Gaussians were used to make the inference process simpler. However, for clustering, a discrete number of vMF distributions are typically used. We propose a Dirichlet Process (DP) to infer the number of clusters in the spherical data setup. The framework is extended to incorporate a nested and a temporal clustering architecture. Throughout this thesis in many cases the posterior cannot be calculated in closed form. Variational Bayesian approximations are derived in this situation for efficient inference. In certain cases further lower bounding of the optimisation function is required in order to perform Variational Bayes. These bounds themselves are novel.

Subjects/Keywords: machine learning; Bayesian inference

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APA (6^th Edition):

Abeywardana, S. (2015). Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/16504

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):

Abeywardana, Sachinthaka. “Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions .” 2015. Thesis, University of Sydney. Accessed September 22, 2019. http://hdl.handle.net/2123/16504.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Abeywardana, Sachinthaka. “Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions .” 2015. Web. 22 Sep 2019.

Vancouver:

Abeywardana S. Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions . [Internet] [Thesis]. University of Sydney; 2015. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/2123/16504.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Abeywardana S. Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions . [Thesis]. University of Sydney; 2015. Available from: http://hdl.handle.net/2123/16504

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Otago

7. Holmes, Tom. Exploring GPS Signal Data and Position Estimation Through Bayesian Inference .

Degree: 2011, University of Otago

URL: http://hdl.handle.net/10523/2032

► A new approach to position determination using the Global Positioning System (GPS) has been developed where post processing of ultra-short sequences of captured GPS satellite… (more)

Subjects/Keywords: GPS; Bayesian; Animal Tracking; Inference

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APA (6^th Edition):

Holmes, T. (2011). Exploring GPS Signal Data and Position Estimation Through Bayesian Inference . (Masters Thesis). University of Otago. Retrieved from http://hdl.handle.net/10523/2032

Chicago Manual of Style (16^th Edition):

Holmes, Tom. “Exploring GPS Signal Data and Position Estimation Through Bayesian Inference .” 2011. Masters Thesis, University of Otago. Accessed September 22, 2019. http://hdl.handle.net/10523/2032.

MLA Handbook (7^th Edition):

Holmes, Tom. “Exploring GPS Signal Data and Position Estimation Through Bayesian Inference .” 2011. Web. 22 Sep 2019.

Vancouver:

Holmes T. Exploring GPS Signal Data and Position Estimation Through Bayesian Inference . [Internet] [Masters thesis]. University of Otago; 2011. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/10523/2032.

Council of Science Editors:

Holmes T. Exploring GPS Signal Data and Position Estimation Through Bayesian Inference . [Masters Thesis]. University of Otago; 2011. Available from: http://hdl.handle.net/10523/2032

University of Miami

8. Abeyruwan, Saminda Wishwajith. PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods.

Degree: MS, Computer Science (Arts and Sciences), 2010, University of Miami

URL: https://scholarlyrepository.miami.edu/oa_theses/28

► An ontology is a formal, explicit specification of a shared conceptualization. Formalizing an ontology for a domain is a tedious and cumbersome process. It is… (more)

Subjects/Keywords: An Ontology; Learning; Bayesian Inference

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APA (6^th Edition):

Abeyruwan, S. W. (2010). PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods. (Thesis). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_theses/28

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):

Abeyruwan, Saminda Wishwajith. “PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods.” 2010. Thesis, University of Miami. Accessed September 22, 2019. https://scholarlyrepository.miami.edu/oa_theses/28.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Abeyruwan, Saminda Wishwajith. “PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods.” 2010. Web. 22 Sep 2019.

Vancouver:

Abeyruwan SW. PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods. [Internet] [Thesis]. University of Miami; 2010. [cited 2019 Sep 22]. Available from: https://scholarlyrepository.miami.edu/oa_theses/28.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Abeyruwan SW. PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods. [Thesis]. University of Miami; 2010. Available from: https://scholarlyrepository.miami.edu/oa_theses/28

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Sydney

9. McCalman, Lachlan Robert. Function Embeddings for Multi-modal Bayesian Inference .

Degree: 2013, University of Sydney

URL: http://hdl.handle.net/2123/12031

► Tractable Bayesian inference is a fundamental challenge in robotics and machine learning. Standard approaches such as Gaussian process regression and Kalman filtering make strong Gaussianity… (more)

▼ Tractable Bayesian inference is a fundamental challenge in robotics and machine learning. Standard approaches such as Gaussian process regression and Kalman filtering make strong Gaussianity assumptions about the underlying distributions. Such assumptions, however, can quickly break down when dealing with complex systems such as the dynamics of a robot or multi-variate spatial models. In this thesis we aim to solve Bayesian regression and filtering problems without making assumptions about the underlying distributions. We develop techniques to produce rich posterior representations for complex, multi-modal phenomena. Our work extends kernel Bayes' rule (KBR), which uses empirical estimates of distributions derived from a set of training samples and embeds them into a high-dimensional reproducing kernel Hilbert space (RKHS). Bayes' rule itself occurs on elements of this space. Our first contribution is the development of an efficient method for estimating posterior density functions from kernel Bayes' rule, applied to both filtering and regression. By embedding fixed-mean mixtures of component distributions, we can efficiently find an approximate pre-image by optimising the mixture weights using a convex quadratic program. The result is a complex, multi-modal posterior representation. Our next contributions are methods for estimating cumulative distributions and quantile estimates from the posterior embedding of kernel Bayes' rule. We examine a number of novel methods, including those based on our density estimation techniques, as well as directly estimating the cumulative through use of the reproducing property of RKHSs. Finally, we develop a novel method for scaling kernel Bayes' rule inference to large datasets, using a reduced-set construction optimised using the posterior likelihood. This method retains the ability to perform multi-output inference, as well as our earlier contributions to represent explicitly non-Gaussian posteriors and quantile estimates.

Subjects/Keywords: Statistics; Inference; Bayesian; Machine Learning

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APA (6^th Edition):

McCalman, L. R. (2013). Function Embeddings for Multi-modal Bayesian Inference . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/12031

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):

McCalman, Lachlan Robert. “Function Embeddings for Multi-modal Bayesian Inference .” 2013. Thesis, University of Sydney. Accessed September 22, 2019. http://hdl.handle.net/2123/12031.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

McCalman, Lachlan Robert. “Function Embeddings for Multi-modal Bayesian Inference .” 2013. Web. 22 Sep 2019.

Vancouver:

McCalman LR. Function Embeddings for Multi-modal Bayesian Inference . [Internet] [Thesis]. University of Sydney; 2013. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/2123/12031.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

McCalman LR. Function Embeddings for Multi-modal Bayesian Inference . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/12031

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Alberta

10. Gonzalez, Ruben. Bayesian Methods for On-Line Gross Error Detection and Compensation.

Degree: MS, Department of Chemical and Materials Engineering, 2010, University of Alberta

URL: https://era.library.ualberta.ca/files/w6634487q

► Data reconciliation and gross error detection are traditional methods toward detecting mass balance inconsistency within process instrument data. These methods use a static approach for… (more)

Subjects/Keywords: Gross Error Detection; Bayesian Inference; Data Reconciliation

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APA (6^th Edition):

Gonzalez, R. (2010). Bayesian Methods for On-Line Gross Error Detection and Compensation. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/w6634487q

Chicago Manual of Style (16^th Edition):

Gonzalez, Ruben. “Bayesian Methods for On-Line Gross Error Detection and Compensation.” 2010. Masters Thesis, University of Alberta. Accessed September 22, 2019. https://era.library.ualberta.ca/files/w6634487q.

MLA Handbook (7^th Edition):

Gonzalez, Ruben. “Bayesian Methods for On-Line Gross Error Detection and Compensation.” 2010. Web. 22 Sep 2019.

Vancouver:

Gonzalez R. Bayesian Methods for On-Line Gross Error Detection and Compensation. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2019 Sep 22]. Available from: https://era.library.ualberta.ca/files/w6634487q.

Council of Science Editors:

Gonzalez R. Bayesian Methods for On-Line Gross Error Detection and Compensation. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/w6634487q

Princeton University

11. Ranganath, Rajesh. Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications .

Degree: PhD, 2017, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01pr76f608w

► Probabilistic generative models are robust to noise, uncover unseen patterns, and make predictions about the future. These models have been used successfully to solve problems… (more)

▼ Probabilistic generative models are robust to noise, uncover unseen patterns, and make predictions about the future. These models have been used successfully to solve problems in neuroscience, astrophysics, genetics, and medicine. The main computational challenge is computing the hidden structure given the data – posterior inference. For most models of interest, computing the posterior distribution requires approximations like variational inference. Variational inference transforms posterior inference into optimization. Classically, this optimization problem was feasible to deploy in only a small fraction of models. This thesis develops black box variational inference. Black box variational inference is a variational inference algorithm that is easy to deploy on a broad class of models and has already found use in models for neuroscience and health care. It makes new kinds of models possible, ones that were too unruly for previous inference methods. One set of models we develop is deep exponential families. Deep exponential families uncover new kinds of hidden pattens while being predictive of future data. Many existing models are deep exponential families. Black box variational inference makes it possible for us to quickly study a broad range of deep exponential families with minimal added effort for each new type of deep exponential family. The ideas around black box variational inference also facilitate new kinds of variational methods. First, we develop hierarchical variational models. Hierarchical variational models improve the approximation quality of variational inference by building higher-fidelity approximations from coarser ones. We show that they help with inference in deep exponential families. Second, we introduce operator variational inference. Operator variational inference delves into the possible distance measures that can be used for the variational optimization problem. We show that this formulation categorizes various variational inference methods and enables variational approximations without tractable densities. By developing black box variational inference, we have opened doors to new models, better posterior approximations, and new varieties of variational inference algorithms. Advisors/Committee Members: Blei, David M (advisor).

Subjects/Keywords: Bayesian Statistics; Machine Learning; Variational Inference

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APA (6^th Edition):

Ranganath, R. (2017). Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01pr76f608w

Chicago Manual of Style (16^th Edition):

Ranganath, Rajesh. “Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications .” 2017. Doctoral Dissertation, Princeton University. Accessed September 22, 2019. http://arks.princeton.edu/ark:/88435/dsp01pr76f608w.

MLA Handbook (7^th Edition):

Ranganath, Rajesh. “Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications .” 2017. Web. 22 Sep 2019.

Vancouver:

Ranganath R. Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Sep 22]. Available from: http://arks.princeton.edu/ark:/88435/dsp01pr76f608w.

Council of Science Editors:

Ranganath R. Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01pr76f608w

Baylor University

12. [No author]. Graphical methods in prior elicitation.

Degree: 2017, Baylor University

URL: http://hdl.handle.net/2104/10111

► Prior elicitation is the process of quantifying an expert's belief in the form of a probability distribution on a parameter(s) to be used in a… (more)

Subjects/Keywords: Bayesian statistics. Prior elicitation. Graphical inference.

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APA (6^th Edition):

author], [. (2017). Graphical methods in prior elicitation. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10111

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):

author], [No. “Graphical methods in prior elicitation. ” 2017. Thesis, Baylor University. Accessed September 22, 2019. http://hdl.handle.net/2104/10111.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

author], [No. “Graphical methods in prior elicitation. ” 2017. Web. 22 Sep 2019.

Vancouver:

author] [. Graphical methods in prior elicitation. [Internet] [Thesis]. Baylor University; 2017. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/2104/10111.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Graphical methods in prior elicitation. [Thesis]. Baylor University; 2017. Available from: http://hdl.handle.net/2104/10111

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

13. Tibbits, Matthew McLean. Parallel Multivariate Slice Sampling.

Degree: MS, Statistics, 2009, Penn State University

URL: https://etda.libraries.psu.edu/catalog/9151

► Slice sampling provides an easily implemented method for constructing a Markov chain Monte Carlo (MCMC) algorithm. However, slice sampling has two major drawbacks: (i) it… (more)

Subjects/Keywords: Bayesian inference; Markov chain Monte Carlo; Para

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APA (6^th Edition):

Tibbits, M. M. (2009). Parallel Multivariate Slice Sampling. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/9151

Chicago Manual of Style (16^th Edition):

Tibbits, Matthew McLean. “Parallel Multivariate Slice Sampling.” 2009. Masters Thesis, Penn State University. Accessed September 22, 2019. https://etda.libraries.psu.edu/catalog/9151.

MLA Handbook (7^th Edition):

Tibbits, Matthew McLean. “Parallel Multivariate Slice Sampling.” 2009. Web. 22 Sep 2019.

Vancouver:

Tibbits MM. Parallel Multivariate Slice Sampling. [Internet] [Masters thesis]. Penn State University; 2009. [cited 2019 Sep 22]. Available from: https://etda.libraries.psu.edu/catalog/9151.

Council of Science Editors:

Tibbits MM. Parallel Multivariate Slice Sampling. [Masters Thesis]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/9151

University of Cambridge

14. Graff, Philip B. Bayesian methods for gravitational waves and neural networks.

Degree: PhD, 2012, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/244270 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190

► Einstein’s general theory of relativity has withstood 100 years of testing and will soon be facing one of its toughest challenges. In a few years… (more)

▼ Einstein’s general theory of relativity has withstood 100 years of testing and will soon be facing one of its toughest challenges. In a few years we expect to be entering the era of the first direct observations of gravitational waves. These are tiny perturbations of space-time that are generated by accelerating matter and affect the measured distances between two points. Observations of these using the laser interferometers, which are the most sensitive length-measuring devices in the world, will allow us to test models of interactions in the strong field regime of gravity and eventually general relativity itself. I apply the tools of Bayesian inference for the examination of gravitational wave data from the LIGO and Virgo detectors. This is used for signal detection and estimation of the source parameters. I quantify the ability of a network of ground-based detectors to localise a source position on the sky for electromagnetic follow-up. Bayesian criteria are also applied to separating real signals from glitches in the detectors. These same tools and lessons can also be applied to the type of data expected from planned space-based detectors. Using simulations from the Mock LISA Data Challenges, I analyse our ability to detect and characterise both burst and continuous signals. The two seemingly different signal types will be overlapping and confused with one another for a space-based detector; my analysis shows that we will be able to separate and identify many signals present. Data sets and astrophysical models are continuously increasing in complexity. This will create an additional computational burden for performing Bayesian inference and other types of data analysis. I investigate the application of the MOPED algorithm for faster parameter estimation and data compression. I find that its shortcomings make it a less favourable candidate for further implementation. The framework of an artificial neural network is a simple model for the structure of a brain which can “learn” functional relationships between sets of inputs and outputs. I describe an algorithm developed for the training of feed-forward networks on pre-calculated data sets. The trained networks can then be used for fast prediction of outputs for new sets of inputs. After demonstrating capabilities on toy data sets, I apply the ability of the network to classifying handwritten digits from the MNIST database and measuring ellipticities of galaxies in the Mapping Dark Matter challenge. The power of neural networks for learning and rapid prediction is also useful in Bayesian inference where the likelihood function is computationally expensive. The new BAMBI algorithm is detailed, in which our network training algorithm is combined with the nested sampling algorithm MULTINEST to provide rapid Bayesian inference. Using samples from the normal inference, a network is trained on the likelihood function and eventually used in its place. This is able to provide significant increase in the speed of Bayesian inference while returning identical results. The…

Subjects/Keywords: 530; Bayesian inference; Gravitational waves; Machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Graff, P. B. (2012). Bayesian methods for gravitational waves and neural networks. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/244270 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190

Chicago Manual of Style (16^th Edition):

Graff, Philip B. “Bayesian methods for gravitational waves and neural networks.” 2012. Doctoral Dissertation, University of Cambridge. Accessed September 22, 2019. https://www.repository.cam.ac.uk/handle/1810/244270 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190.

MLA Handbook (7^th Edition):

Graff, Philip B. “Bayesian methods for gravitational waves and neural networks.” 2012. Web. 22 Sep 2019.

Vancouver:

Graff PB. Bayesian methods for gravitational waves and neural networks. [Internet] [Doctoral dissertation]. University of Cambridge; 2012. [cited 2019 Sep 22]. Available from: https://www.repository.cam.ac.uk/handle/1810/244270 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190.

Council of Science Editors:

Graff PB. Bayesian methods for gravitational waves and neural networks. [Doctoral Dissertation]. University of Cambridge; 2012. Available from: https://www.repository.cam.ac.uk/handle/1810/244270 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190

University of Houston

15. Bhardwaj, Manisha 1986-. Visual decision making in the presence of stimulus and measurement correlations.

Degree: Mathematics, Department of, 2013, University of Houston

URL: http://hdl.handle.net/10657/1239

► Our brains process sensory information to infer the state of the world. However, the input from our senses is noisy, which may lead to errors… (more)

▼ Our brains process sensory information to infer the state of the world. However, the input from our senses is noisy, which may lead to errors in perceptual judgements. A number of theoretical studies have modeled perception as a process of probabilistic inference that involves making decisions based on uncertain evidence. Bayesian optimality is a general principle of probabilistic inference that has been successfully used to build quantitative models of perception. In addition, several experimental studies show that human observers make best possible decisions, and hence exhibit close to Bayes-optimal behavior on various visual perceptual tasks such as visual search, sameness judgement, and change detection. However, the impact of structured stimuli on decision-making remains largely unexplored. Moreover, the sensory measurements can themselves be strongly correlated to produce a structured representation of the stimulus input. These measurement correlations can interact with the structure of the external input in many possible ways and should not be considered in isolation. In this work, we focus on visual search task to examine how visual perception is affected by structured input. We analyze the responses of subjects on a target detection experiment where the stimulus orientations were generated with varying strength of correlations across different experimental sessions. We fit several models to the experimental data using maximum-likelihood parameter estimation. We use rigorous model selection to find that human observers take into account stimulus correlations in detecting a target. However, they behave suboptimally in inferring the correct stimulus correlations that were used in the experiment. We find that perhaps observers treat the partial stimulus correlations identically and behave differently when the stimuli are perfectly correlated. We also describe how the relation between measurement and stimulus correlations affects the performance of an ideal Bayesian observer in a family of target detection tasks. We find that the effect of measurement correlations depends on its interaction with stimulus correlations and other statistical structure parameters. Measurement correlations always improves the performance of the ideal observer on a detection task with multiple targets; whereas in the case of single target, the impact is significant only in the presence of strong external structure. Advisors/Committee Members: Josić, Krešimir (advisor), Nicol, Matthew (committee member), Timofeyev, Ilya (committee member), Ma, Wei Ji (committee member).

Subjects/Keywords: Bayesian inference; model comparison; correlations; target detection

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APA (6^th Edition):

Bhardwaj, M. 1. (2013). Visual decision making in the presence of stimulus and measurement correlations. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/1239

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):

Bhardwaj, Manisha 1986-. “Visual decision making in the presence of stimulus and measurement correlations.” 2013. Thesis, University of Houston. Accessed September 22, 2019. http://hdl.handle.net/10657/1239.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bhardwaj, Manisha 1986-. “Visual decision making in the presence of stimulus and measurement correlations.” 2013. Web. 22 Sep 2019.

Vancouver:

Bhardwaj M1. Visual decision making in the presence of stimulus and measurement correlations. [Internet] [Thesis]. University of Houston; 2013. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/10657/1239.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bhardwaj M1. Visual decision making in the presence of stimulus and measurement correlations. [Thesis]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/1239

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

16. Jin, Yingyan. Supervised learning for back analysis of excavations in the observational method.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/275587 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744808

► In the past few decades, demand for construction in underground spaces has increased dramatically in urban areas with high population densities. However, the impact of… (more)

▼ In the past few decades, demand for construction in underground spaces has increased dramatically in urban areas with high population densities. However, the impact of the construction of underground structures on surrounding infrastructure raises concerns since movements caused by deep excavations might damage adjacent buildings. Unfortunately, the prediction of geotechnical behaviour is difficult due to uncertainties and lack of information of on the underground environment. Therefore, to ensure safety, engineers tend to choose very conservative designs that result in requiring unnecessary material and longer construction time. The observational method, which was proposed by Peck in 1969, and formalised in Eurocode 7 in 1987, provides a way to avoid such redundancy by modifying the design based on the knowledge gathered during construction. The review process within the observational method is recognised as back analysis. Supervised learning can aid in this process, providing a systematic procedure to assess soil parameters based on monitoring data and prediction of the ground response. A probabilistic model is developed in this research to account for the uncertainties in the problem. Sequential Bayesian inference is used to update the soil parameters at each excavation stage when observations are available. The accuracy of the prediction for future stages improves at each stage. Meanwhile, the uncertainty contained in the prediction decreases, and therefore the confidence on the corresponding design also increases. Moreover, the Bayesian method integrates subjective engineering experience and objective observations in a rational and quantitative way, which enables the model to update soil parameters even when the amount of data is very limited. It also allows the use of the knowledge learnt from comparable ground conditions, which is particularly useful in the absence of site-specific information on ground conditions. Four probabilistic models are developed in this research. The first two incorporate empirical excavation design methods. These simple models are used to examine the practicality of the approach with several cases. The next two are coupled with a program called FREW, which is able to simulate the excavation process, still in a relatively simplistic way. The baseline model with simple assumptions on model error and another one is a more sophisticated model considering measurement error and spatial relationships among the observations. Their efficiency and accuracy are verified using a synthetic case and tested based on a case history from the London Crossrail project. In the end, the models are compared and their flexibility in different cases is discussed.

Subjects/Keywords: Bayesian inference; Excavations; Observational method; Back analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Jin, Y. (2018). Supervised learning for back analysis of excavations in the observational method. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/275587 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744808

Chicago Manual of Style (16^th Edition):

Jin, Yingyan. “Supervised learning for back analysis of excavations in the observational method.” 2018. Doctoral Dissertation, University of Cambridge. Accessed September 22, 2019. https://www.repository.cam.ac.uk/handle/1810/275587 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744808.

MLA Handbook (7^th Edition):

Jin, Yingyan. “Supervised learning for back analysis of excavations in the observational method.” 2018. Web. 22 Sep 2019.

Vancouver:

Jin Y. Supervised learning for back analysis of excavations in the observational method. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Sep 22]. Available from: https://www.repository.cam.ac.uk/handle/1810/275587 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744808.

Council of Science Editors:

Jin Y. Supervised learning for back analysis of excavations in the observational method. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/275587 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744808

Cornell University

17. Wan, Muting. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics .

Degree: 2015, Cornell University

URL: http://hdl.handle.net/1813/39389

► In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter… (more)

▼ In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter sparsity is substantial. Shrinkage estimation allows strength borrowing across features in light of the parallel nature of multiple hypothesis tests. Important examples that incorporate shrinkage estimation and finite mixture model for sparse classification include the hierarchical model in Smyth (2004) and the explicit mixture model in Bar et al. (2010) for Bayesian microarray analysis. Classification with finite mixture models is based on the posterior expectation of latent indicator variables. These quantities are typically estimated using the expectation-maximization (EM) algorithm in an empirical Bayes approach or Markov chain Monte Carlo (MCMC) in a fully Bayesian approach. MCMC is limited in applicability where high-dimensional data are involved because its sampling-based nature leads to slow computations and hard-to-monitor convergence. In a fully Bayesian framework, we investigate the feasibility and performance of variational Bayes (VB) approximation and apply the VB approach to fully Bayesian versions of several finite mixture models that have been proposed in bioinformatics. We find that it achieves desirable speed and accuracy in sparse classification with hierarchical mixture models for high-dimensional data. Another example of sparse classification in bioinformatics solvable via model-based approaches is expression quantitative trait loci (eQTL) detection, in which determining whether association between a gene and any given single nucleotide polymorphism (SNP) is significant is regarded as classifying genes as null or non-null with respect to the given SNP. High-dimensionality of the data not only causes difficulties in computations, but also renders the confounding impact of unwanted variation in the data irrefutable. Model-based approaches that account for unwanted variation by incorporating a factor analysis term representing hidden factors and their effects have been adopted in applications such as differential analysis and eQTL detection. HEFT (Gao et al., 2014) is a fast approach for model-based eQTL identification while simultaneously learning hidden effects. We develop a hierarchical mixture model-based empirical Bayes approach for sparse classification while simultaneously accounting for unwanted variation, as well as a family of model-based approaches that are its simplifications with the aim of attractive computational efficiency. We investigate feasibility and performance of these model-based approaches in comparison with HEFT using several real data examples in bioinformatics. Advisors/Committee Members: Hooker, Giles J. (committeeMember), Wells, Martin Timothy (committeeMember).

Subjects/Keywords: Bayesian inference; Linear mixed models; Bioinformatics

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APA (6^th Edition):

Wan, M. (2015). Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/39389

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):

Wan, Muting. “Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics .” 2015. Thesis, Cornell University. Accessed September 22, 2019. http://hdl.handle.net/1813/39389.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wan, Muting. “Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics .” 2015. Web. 22 Sep 2019.

Vancouver:

Wan M. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics . [Internet] [Thesis]. Cornell University; 2015. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/1813/39389.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wan M. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics . [Thesis]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/39389

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

North Carolina State University

18. DiCasoli, Carl Matthew. Bayesian Regression Methods for Crossing Survival Curves.

Degree: PhD, Statistics, 2009, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/4743

► In survival data analysis, the proportional hazards (PH), accelerated failure time (AFT), and proportional odds (PO) models are commonly used semiparametric models for the comparison… (more)

▼ In survival data analysis, the proportional hazards (PH), accelerated failure time (AFT), and proportional odds (PO) models are commonly used semiparametric models for the comparison of survivability in subjects. These models assume that the survival curves do not cross. However, in some clinical applications, the survival curves pertaining to the two groups of subjects under the study may cross each other, especially for long-duration studies. Hence, these three models stated above may no longer be suitable for making inference Yang and Prentice (2005) proposed a model which separately models the short-term and long-term hazard ratios nesting both PH and PO. This feature allows for the survival functions to cross. First, we study the estimation procedure in the Yang-Prentice model with regards to the two-sample case. We propose two different approaches: (1) Bayesian bootstrap and (2) smoothing methods. The first approach involves Bayesian bootstrap with likelihoods corresponding to binomial and Poisson forms while the second approach involves kernel smoothing methods as well as smoothing spline methods. A simulation is conducted to compare various methods under the two-sample case. Next, we extend the Yang-Prentice model to a regression version involving predictors and examine three likelihood approaches including Poisson form, pseudo-likelihood, and Bayesian smoothing. The effects of model misspecification on asymptotic relative efficiency are also studied empirically. The results from simulation studies indicate that the PH, AFT, and PO models are not robust to model misspecifications when the survival functions are allowed to cross. Finally, we calculate the marginal density via variational methods to determine the Bayes factor. Either a full Bayesian or Bayesian approach is implemented to perform model selection. Both approaches accurately identify the correct model, even under slight misspecification, and are computationally more efficient than MCMC techniques. Advisors/Committee Members: Dr. Charles Apperson, Committee Member (advisor), Dr. Wenbin Lu, Committee Member (advisor), Dr. Brian Reich, Committee Member (advisor), Dr. Subhashis Ghosal, Committee Co-Chair (advisor), Dr. Sujit Ghosh, Committee Chair (advisor).

Subjects/Keywords: variational methods; Bayesian inference; survival analysis

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APA (6^th Edition):

DiCasoli, C. M. (2009). Bayesian Regression Methods for Crossing Survival Curves. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/4743

Chicago Manual of Style (16^th Edition):

DiCasoli, Carl Matthew. “Bayesian Regression Methods for Crossing Survival Curves.” 2009. Doctoral Dissertation, North Carolina State University. Accessed September 22, 2019. http://www.lib.ncsu.edu/resolver/1840.16/4743.

MLA Handbook (7^th Edition):

DiCasoli, Carl Matthew. “Bayesian Regression Methods for Crossing Survival Curves.” 2009. Web. 22 Sep 2019.

Vancouver:

DiCasoli CM. Bayesian Regression Methods for Crossing Survival Curves. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2019 Sep 22]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4743.

Council of Science Editors:

DiCasoli CM. Bayesian Regression Methods for Crossing Survival Curves. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4743

19. Feldman, Naomi H. Interactions between word and speech sound categorization in language acquisition.

Degree: PhD, Cognitive Sciences, 2011, Brown University

URL: https://repository.library.brown.edu/studio/item/bdr:11219/

► Infants learn to segment words from fluent speech during the same period as they learn native language phonetic categories, yet accounts of phonetic category acquisition… (more)

▼ Infants learn to segment words from fluent speech during the same period as they learn native language phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. This work uses computational and behavioral methods to explore the hypothesis that the words infants segment from fluent speech can provide useful cues to guide phonetic category acquisition. An interactive Bayesian learning model is created to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. This model is compared with two models that learn phonetic categories only from distributions of sounds in acoustic space. Simulations show that word-level information can successfully disambiguate overlapping English vowel categories, leading to more robust category learning than distributional information alone. Next, two experiments test whether human learners can make use of the word-level cues required for this type of interactive learning. These experiments demonstrate that adult learners are sensitive to cooccurrence patterns of sounds in acoustic word tokens of an unfamiliar language. However, human learners appear to treat the patterns differently when words are heard in isolation versus when they are heard in fluent speech, adopting either a word-level or a phonological interpretation depending on experimental context. These behavioral results partially support the predictions of the model but underscore the complexity of the phonetic category learning problem. Together, the computational and behavioral results suggest that phonetic category learning can be better understood in conjunction with other contemporaneous learning processes and that simultaneous learning of multiple layers of linguistic structure can potentially make the language acquisition problem more tractable. Advisors/Committee Members: Morgan, James (Director), Griffiths, Thomas (Reader), Blumstein, Sheila (Reader).

Subjects/Keywords: language acquisition; phonetic category learning; Bayesian inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Feldman, N. H. (2011). Interactions between word and speech sound categorization in language acquisition. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:11219/

Chicago Manual of Style (16^th Edition):

Feldman, Naomi H. “Interactions between word and speech sound categorization in language acquisition.” 2011. Doctoral Dissertation, Brown University. Accessed September 22, 2019. https://repository.library.brown.edu/studio/item/bdr:11219/.

MLA Handbook (7^th Edition):

Feldman, Naomi H. “Interactions between word and speech sound categorization in language acquisition.” 2011. Web. 22 Sep 2019.

Vancouver:

Feldman NH. Interactions between word and speech sound categorization in language acquisition. [Internet] [Doctoral dissertation]. Brown University; 2011. [cited 2019 Sep 22]. Available from: https://repository.library.brown.edu/studio/item/bdr:11219/.

Council of Science Editors:

Feldman NH. Interactions between word and speech sound categorization in language acquisition. [Doctoral Dissertation]. Brown University; 2011. Available from: https://repository.library.brown.edu/studio/item/bdr:11219/

Tampere University

20. Lu, Chien. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .

Degree: 2018, Tampere University

URL: https://trepo.tuni.fi/handle/10024/104066

► Entropy estimation is an important technique to summarize the uncertainty of a distribution underlying a set of samples. It ties to important research problems in… (more)

Subjects/Keywords: entropy estimation; nonparametric estimator; Bayesian inference

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APA (6^th Edition):

Lu, C. (2018). An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/104066

Chicago Manual of Style (16^th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Masters Thesis, Tampere University. Accessed September 22, 2019. https://trepo.tuni.fi/handle/10024/104066.

MLA Handbook (7^th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Web. 22 Sep 2019.

Vancouver:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Internet] [Masters thesis]. Tampere University; 2018. [cited 2019 Sep 22]. Available from: https://trepo.tuni.fi/handle/10024/104066.

Council of Science Editors:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Masters Thesis]. Tampere University; 2018. Available from: https://trepo.tuni.fi/handle/10024/104066

University of Sydney

21. Babbar, Sakshi. Inferring Anomalies from Data using Bayesian Networks .

Degree: 2013, University of Sydney

URL: http://hdl.handle.net/2123/9371

► Existing studies on data mining has largely focused on the design of measures and algorithms to identify outliers in large and high dimensional categorical and… (more)

Subjects/Keywords: Bayesian networks; outlier; causal inference; knowledge discovery

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APA (6^th Edition):

Babbar, S. (2013). Inferring Anomalies from Data using Bayesian Networks . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/9371

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):

Babbar, Sakshi. “Inferring Anomalies from Data using Bayesian Networks .” 2013. Thesis, University of Sydney. Accessed September 22, 2019. http://hdl.handle.net/2123/9371.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Babbar, Sakshi. “Inferring Anomalies from Data using Bayesian Networks .” 2013. Web. 22 Sep 2019.

Vancouver:

Babbar S. Inferring Anomalies from Data using Bayesian Networks . [Internet] [Thesis]. University of Sydney; 2013. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/2123/9371.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Babbar S. Inferring Anomalies from Data using Bayesian Networks . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/9371

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universitat de Valencia

22. Martínez Minaya, Joaquín. Recent statistical advances and applications of species distribution modeling .

Degree: 2019, Universitat de Valencia

URL: http://hdl.handle.net/10550/71315

► En el mundo en que vivimos, producimos aproximadamente 2.5 quintillones de bytes de datos por día. Esta enorme cantidad de datos proviene de las redes… (more)

▼ En el mundo en que vivimos, producimos aproximadamente 2.5 quintillones de bytes de datos por día. Esta enorme cantidad de datos proviene de las redes sociales, Internet, satélites, etc. Todos estos datos, que se pueden registrar en el tiempo o en el espacio, son información que puede ayudarnos a comprender la propagación de una enfermedad, el movimiento de especies o el cambio climático. El uso de modelos estadísticos complejos ha aumentado recientemente en el contexto del estudio de la distribución de especies. Esta complejidad ha hecho que los procesos inferenciales y predictivos sean difíciles de realizar. El enfoque bayesiano se ha convertido en una buena opción para lidiar con estos modelos, debido a la facilidad con la que se puede incorporar la información previa, junto con el hecho de que proporciona una estimación de la incertidumbre más realista y precisa. En esta tesis, mostramos una visión actualizada del uso de las últimas herramientas estadísticas que han surgido en la aplicación de modelos de distribución de especies (SDMs) en contextos reales desde una perspectiva bayesiana, y desarrollamos nuevas herramientas metodológicas para resolver algunos problemas estadísticos que aparecieron en ese proceso. Con respecto a la aplicación de las últimas herramientas estadísticas en el contexto de los SDMs, los objetivos específicos han sido modelizar la producción de ascosporas Plurivorosphaerella nawae en la hojarasca de caqui; estudiar los factores espaciales y climáticos asociados con la distribución de la mancha negra de los cítricos causada por el hongo Phyllosticta citricarpa; analizar los efectos de la estructura genética y la autocorrelación espacial en los cambios de rango de distribución de las especies; y estudiar la distribución del delfín mular (Tursiops truncatus). Dos objetivos han marcado la parte más metodológica de la tesis: una revisión centrada en los problemas estadísticos en SDMs y la implementación de la regresión de Dirichlet bayesiana en el contexto de la aproximación de Laplace anidada integrada (INLA). La tesis que aquí presentamos es un compendio de ocho artículos y a continuación mostramos su estructura. En los cuatro primeros capítulos presentamos una introducción general que incluye una descripción de los objetivos (Capítulo 1), la base de la metodología empleada (Capítulos 2 y 3) y una descripción de los resultados obtenidos (Capítulo 4). En los ocho capítulos siguientes, mostramos todos los artículos que componen este compendio. Y por último, incluimos el Capítulo 13, donde se presentan algunas conclusiones y líneas futuras de investigación, seguido de una bibliografía genérica correspondiente a los capítulos introductorios. Advisors/Committee Members: Conesa Guillén, David (advisor).

Subjects/Keywords: bayesian inference; inla; species distribution models; geostatistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Martínez Minaya, J. (2019). Recent statistical advances and applications of species distribution modeling . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/71315

Chicago Manual of Style (16^th Edition):

Martínez Minaya, Joaquín. “Recent statistical advances and applications of species distribution modeling .” 2019. Doctoral Dissertation, Universitat de Valencia. Accessed September 22, 2019. http://hdl.handle.net/10550/71315.

MLA Handbook (7^th Edition):

Martínez Minaya, Joaquín. “Recent statistical advances and applications of species distribution modeling .” 2019. Web. 22 Sep 2019.

Vancouver:

Martínez Minaya J. Recent statistical advances and applications of species distribution modeling . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2019. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/10550/71315.

Council of Science Editors:

Martínez Minaya J. Recent statistical advances and applications of species distribution modeling . [Doctoral Dissertation]. Universitat de Valencia; 2019. Available from: http://hdl.handle.net/10550/71315

University of Texas – Austin

23. Gillett, Carlos Townes. A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions.

Degree: MSin Statistics, Statistics, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/32494

► This report compares the convergence behavior of the Metropolis-Hastings and an alternative Markov Chain Monte Carlo sampling algorithm targeting unnormalized, discrete distributions with countably infinite… (more)

Subjects/Keywords: Metropolis-Hastings; Bayesian inference; Unnormalized probabilities

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APA (6^th Edition):

Gillett, C. T. (2015). A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32494

Chicago Manual of Style (16^th Edition):

Gillett, Carlos Townes. “A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions.” 2015. Masters Thesis, University of Texas – Austin. Accessed September 22, 2019. http://hdl.handle.net/2152/32494.

MLA Handbook (7^th Edition):

Gillett, Carlos Townes. “A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions.” 2015. Web. 22 Sep 2019.

Vancouver:

Gillett CT. A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/2152/32494.

Council of Science Editors:

Gillett CT. A comparison of two Markov Chain Monte Carlo methods for sampling from unnormalized discrete distributions. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32494

University of Texas – Austin

24. Zhang, Michael Minyi. Scalable inference for Bayesian non-parametrics.

Degree: PhD, Statistics, 2018, University of Texas – Austin

URL: http://hdl.handle.net/2152/65734

► Bayesian non-parametric models, despite their theoretical elegance, face a serious computational burden that prevents their use in serious "big data'' scenarios. Furthermore, we cannot expect… (more)

Subjects/Keywords: Bayesian non-parametrics; Scalable inference; Machine learning

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APA (6^th Edition):

Zhang, M. M. (2018). Scalable inference for Bayesian non-parametrics. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65734

Chicago Manual of Style (16^th Edition):

Zhang, Michael Minyi. “Scalable inference for Bayesian non-parametrics.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed September 22, 2019. http://hdl.handle.net/2152/65734.

MLA Handbook (7^th Edition):

Zhang, Michael Minyi. “Scalable inference for Bayesian non-parametrics.” 2018. Web. 22 Sep 2019.

Vancouver:

Zhang MM. Scalable inference for Bayesian non-parametrics. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/2152/65734.

Council of Science Editors:

Zhang MM. Scalable inference for Bayesian non-parametrics. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65734

University of Cambridge

25. Matthews, Alexander Graeme de Garis. Scalable Gaussian process inference using variational methods.

Degree: PhD, 2017, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/278022 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753312

► Gaussian processes can be used as priors on functions. The need for a flexible, principled, probabilistic model of functional relations is common in practice. Consequently,… (more)

▼ Gaussian processes can be used as priors on functions. The need for a flexible, principled, probabilistic model of functional relations is common in practice. Consequently, such an approach is demonstrably useful in a large variety of applications. Two challenges of Gaussian process modelling are often encountered. These are dealing with the adverse scaling with the number of data points and the lack of closed form posteriors when the likelihood is non-Gaussian. In this thesis, we study variational inference as a framework for meeting these challenges. An introductory chapter motivates the use of stochastic processes as priors, with a particular focus on Gaussian process modelling. A section on variational inference reviews the general definition of Kullback-Leibler divergence. The concept of prior conditional matching that is used throughout the thesis is contrasted to classical approaches to obtaining tractable variational approximating families. Various theoretical issues arising from the application of variational inference to the infinite dimensional Gaussian process setting are settled decisively. From this theory we are able to give a new argument for existing approaches to variational regression that settles debate about their applicability. This view on these methods justifies the principled extensions found in the rest of the work. The case of scalable Gaussian process classification is studied, both for its own merits and as a case study for non-Gaussian likelihoods in general. Using the resulting algorithms we find credible results on datasets of a scale and complexity that was not possible before our work. An extension to include Bayesian priors on model hyperparameters is studied alongside a new inference method that combines the benefits of variational sparsity and MCMC methods. The utility of such an approach is shown on a variety of example modelling tasks. We describe GPflow, a new Gaussian process software library that uses TensorFlow. Implementations of the variational algorithms discussed in the rest of the thesis are included as part of the software. We discuss the benefits of GPflow when compared to other similar software. Increased computational speed is demonstrated in relevant, timed, experimental comparisons.

Subjects/Keywords: Gaussian process; Variational inference; Machine learning; Statistics; Bayesian inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Matthews, A. G. d. G. (2017). Scalable Gaussian process inference using variational methods. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/278022 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753312

Chicago Manual of Style (16^th Edition):

Matthews, Alexander Graeme de Garis. “Scalable Gaussian process inference using variational methods.” 2017. Doctoral Dissertation, University of Cambridge. Accessed September 22, 2019. https://www.repository.cam.ac.uk/handle/1810/278022 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753312.

MLA Handbook (7^th Edition):

Matthews, Alexander Graeme de Garis. “Scalable Gaussian process inference using variational methods.” 2017. Web. 22 Sep 2019.

Vancouver:

Matthews AGdG. Scalable Gaussian process inference using variational methods. [Internet] [Doctoral dissertation]. University of Cambridge; 2017. [cited 2019 Sep 22]. Available from: https://www.repository.cam.ac.uk/handle/1810/278022 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753312.

Council of Science Editors:

Matthews AGdG. Scalable Gaussian process inference using variational methods. [Doctoral Dissertation]. University of Cambridge; 2017. Available from: https://www.repository.cam.ac.uk/handle/1810/278022 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753312

University of Waterloo

26. Hincks, Ian. Exploring Practical Methodologies for the Characterization and Control of Small Quantum Systems.

Degree: 2018, University of Waterloo

URL: http://hdl.handle.net/10012/13711

► We explore methodologies for characterizing and controlling small quantum systems. We are interested in starting with a description of a quantum system, designing estimators for… (more)

Subjects/Keywords: quantum; quantum control; quantum information; bayesian inference; benchmarking; quantum characterization; inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hincks, I. (2018). Exploring Practical Methodologies for the Characterization and Control of Small Quantum Systems. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13711

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):

Hincks, Ian. “Exploring Practical Methodologies for the Characterization and Control of Small Quantum Systems.” 2018. Thesis, University of Waterloo. Accessed September 22, 2019. http://hdl.handle.net/10012/13711.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hincks, Ian. “Exploring Practical Methodologies for the Characterization and Control of Small Quantum Systems.” 2018. Web. 22 Sep 2019.

Vancouver:

Hincks I. Exploring Practical Methodologies for the Characterization and Control of Small Quantum Systems. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/10012/13711.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hincks I. Exploring Practical Methodologies for the Characterization and Control of Small Quantum Systems. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13711

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Australian National University

27. Dawkins, Mark Walter. Melded Bayesian Inference for Stochastic Theoretical Models with Applications in Agent Based Modelling .

Degree: 2017, Australian National University

URL: http://hdl.handle.net/1885/147060

► Bayesian melding is extended for applications to stochastic theoretical models. Agent Based models, a class of stochastic theoretical models, are investigated and it is found… (more)

Subjects/Keywords: Bayesian Melding; Bayesian Inference; Agent Based Modelling; Simulation; Stochastic Modelling

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APA (6^th Edition):

Dawkins, M. W. (2017). Melded Bayesian Inference for Stochastic Theoretical Models with Applications in Agent Based Modelling . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/147060

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):

Dawkins, Mark Walter. “Melded Bayesian Inference for Stochastic Theoretical Models with Applications in Agent Based Modelling .” 2017. Thesis, Australian National University. Accessed September 22, 2019. http://hdl.handle.net/1885/147060.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dawkins, Mark Walter. “Melded Bayesian Inference for Stochastic Theoretical Models with Applications in Agent Based Modelling .” 2017. Web. 22 Sep 2019.

Vancouver:

Dawkins MW. Melded Bayesian Inference for Stochastic Theoretical Models with Applications in Agent Based Modelling . [Internet] [Thesis]. Australian National University; 2017. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/1885/147060.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dawkins MW. Melded Bayesian Inference for Stochastic Theoretical Models with Applications in Agent Based Modelling . [Thesis]. Australian National University; 2017. Available from: http://hdl.handle.net/1885/147060

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Texas A&M University

28. Chen, Ting. Estimation and Detection of Multivariate Gene Regulatory Relationships.

Degree: 2013, Texas A&M University

URL: http://hdl.handle.net/1969.1/151702

► The Coefficient of Determination (CoD) plays an important role in Genomics problems, for instance, in the inference of gene regulatory networks from gene- expression data.… (more)

▼ The Coefficient of Determination (CoD) plays an important role in Genomics problems, for instance, in the inference of gene regulatory networks from gene- expression data. However, the inference theory about CoD has not been investigated systematically. In this dissertation, we study the inference of discrete CoD from both frequentist and Bayesian perspectives, with its applications to system identification problems in Genomics. From a frequentist viewpoint, we provide a theoretical framework for CoD estimation by introducing nonparametric CoD estimators and parametric maximum-likelihood (ML) CoD estimators based on static and dynamical Boolean models. Inference algorithms are developed to discover gene regulatory relationships, and numerical examples are provided to validate preferable performance of the ML approach with access to sufficient prior knowledge. To make the applications of the CoD independent of user-selectable thresholds, we describe rigorous multiple testing procedures to investigate significant regulatory relation- ships among genes using the discrete CoD, and to discover canalyzing genes using the intrinsically multivariate prediction (IMP) criterion. We develop practical statistic tools that are open to the scientific community. On the other hand, we propose a Bayesian framework for the inference of the CoD across a parametrized family of joint distributions between target and predictors. Examples of applications of the Bayesian approach are provided against those of nonparametric and parametric approaches by using synthetic data. We have found that, with applications to system identification problems in Genomics, both parametric and Bayesian CoD estimation approaches outperform the nonparametric approaches. Hence, we conclude that parametric and Bayesian estimation approaches are preferred when we have partial knowledge about gene regulation. On the other hand, we have shown that the two proposed statistical testing frameworks can detect well-known gene regulation and canalyzing genes like p53 and DUSP1 from real data sets, respectively. This indicates that our methodology could serve as a promising tool for the detection of potential gene regulatory relationships and canalyzing genes. In one word, this dissertation is intended to serve as foundation for a detailed study of applications of CoD estimation in Genomics and related fields. Advisors/Committee Members: Braga-Neto, Ulisses M. (advisor), Dougherty, Edward R. (committee member), Chamberland-Tremblay, Jean-Francois (committee member), Wehrly, Thomas (committee member).

Subjects/Keywords: Coefficient of Determination; Frequentist Inference; Maximum-Likelihood Estimation; Multiple Testing Procedures; Bayesian Inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, T. (2013). Estimation and Detection of Multivariate Gene Regulatory Relationships. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/151702

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):

Chen, Ting. “Estimation and Detection of Multivariate Gene Regulatory Relationships.” 2013. Thesis, Texas A&M University. Accessed September 22, 2019. http://hdl.handle.net/1969.1/151702.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Ting. “Estimation and Detection of Multivariate Gene Regulatory Relationships.” 2013. Web. 22 Sep 2019.

Vancouver:

Chen T. Estimation and Detection of Multivariate Gene Regulatory Relationships. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/1969.1/151702.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen T. Estimation and Detection of Multivariate Gene Regulatory Relationships. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/151702

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

29. Jin, Junyang. Novel methods for biological network inference : an application to circadian Ca2+ signaling network.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/285323 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763710

► Biological processes involve complex biochemical interactions among a large number of species like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering… (more)

▼ Biological processes involve complex biochemical interactions among a large number of species like cells, RNA, proteins and metabolites. Learning these interactions is essential to interfering artificially with biological processes in order to, for example, improve crop yield, develop new therapies, and predict new cell or organism behaviors to genetic or environmental perturbations. For a biological process, two pieces of information are of most interest. For a particular species, the first step is to learn which other species are regulating it. This reveals topology and causality. The second step involves learning the precise mechanisms of how this regulation occurs. This step reveals the dynamics of the system. Applying this process to all species leads to the complete dynamical network. Systems biology is making considerable efforts to learn biological networks at low experimental costs. The main goal of this thesis is to develop advanced methods to build models for biological networks, taking the circadian system of Arabidopsis thaliana as a case study. A variety of network inference approaches have been proposed in the literature to study dynamic biological networks. However, many successful methods either require prior knowledge of the system or focus more on topology. This thesis presents novel methods that identify both network topology and dynamics, and do not depend on prior knowledge. Hence, the proposed methods are applicable to general biological networks. These methods are initially developed for linear systems, and, at the cost of higher computational complexity, can also be applied to nonlinear systems. Overall, we propose four methods with increasing computational complexity: one-to-one, combined group and element sparse Bayesian learning (GESBL), the kernel method and reversible jump Markov chain Monte Carlo method (RJMCMC). All methods are tested with challenging dynamical network simulations (including feedback, random networks, different levels of noise and number of samples), and realistic models of circadian system of Arabidopsis thaliana. These simulations show that, while the one-to-one method scales to the whole genome, the kernel method and RJMCMC method are superior for smaller networks. They are robust to tuning variables and able to provide stable performance. The simulations also imply the advantage of GESBL and RJMCMC over the state-of-the-art method. We envision that the estimated models can benefit a wide range of research. For example, they can locate biological compounds responsible for human disease through mathematical analysis and help predict the effectiveness of new treatments.

Subjects/Keywords: systems biology; network inference; system identification; sparse networks; machine learning; Bayesian inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Jin, J. (2018). Novel methods for biological network inference : an application to circadian Ca2+ signaling network. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/285323 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763710

Chicago Manual of Style (16^th Edition):

Jin, Junyang. “Novel methods for biological network inference : an application to circadian Ca2+ signaling network.” 2018. Doctoral Dissertation, University of Cambridge. Accessed September 22, 2019. https://www.repository.cam.ac.uk/handle/1810/285323 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763710.

MLA Handbook (7^th Edition):

Jin, Junyang. “Novel methods for biological network inference : an application to circadian Ca2+ signaling network.” 2018. Web. 22 Sep 2019.

Vancouver:

Jin J. Novel methods for biological network inference : an application to circadian Ca2+ signaling network. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Sep 22]. Available from: https://www.repository.cam.ac.uk/handle/1810/285323 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763710.

Council of Science Editors:

Jin J. Novel methods for biological network inference : an application to circadian Ca2+ signaling network. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/285323 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763710

EPFL

30. Ağir, Berker. Context and Semantic Aware Location Privacy.

Degree: 2016, EPFL

URL: http://infoscience.epfl.ch/record/219121

► With ever-increasing computational power, and improved sensing and communication capabilities, smart devices have altered and enhanced the way we process, perceive and interact with information.… (more)

▼ With ever-increasing computational power, and improved sensing and communication capabilities, smart devices have altered and enhanced the way we process, perceive and interact with information. Personal and contextual data is tracked and stored extensively on these devices and, oftentimes, ubiquitously sent to online service providers. This routine is proving to be quite privacy-invasive, since these service providers mine the data they collect in order to infer more and more personal information about users. Protecting privacy in the rise of mobile applications is a critical challenge. The continuous tracking of users with location- and time-stamps expose their private lives at an alarming level. Location traces can be used to infer intimate aspects of users' lives such as interests, political orientation, religious beliefs, and even more. Traditional approaches to protecting privacy fail to meet users' expectations due to simplistic adversary models and the lack of a multi-dimensional awareness. In this thesis, the development of privacy-protection approaches is pushed further by (i) adapting to concrete adversary capabilities and (ii) investigating the threat of strong adversaries that exploit location semantics. We first study user mobility and spatio-temporal correlations in continuous disclosure scenarios (e.g., sensing applications), where the more frequently a user discloses her location, the more difficult it becomes to protect. To counter this threat, we develop adversary- and mobility-aware privacy protection mechanisms that aim to minimize an adversary's exploitation of user mobility. We demonstrate that a privacy protection mechanism must actively evaluate privacy risks in order to adapt its protection parameters. We further develop an Android library that provides on-device location privacy evaluation and enables any location-based application to support privacy-preserving services. We also implement an adversary-aware protection mechanism in this library with semantic-based privacy settings. Furthermore, we study the effects of an adversary that exploits location semantics in order to strengthen his attacks on user traces. Such extensive information is available to an adversary via maps of points of interest, but also from users themselves. Typically, users of online social networks want to announce their whereabouts to their circles. They do so mostly, if not always, by sharing the type of their location along with the geographical coordinates. We formalize this setting and by using Bayesian inference show that if location semantics of traces is disclosed, users' privacy levels drop considerably. Moreover, we study the time-of-day information and its relation to location semantics. We reveal that an adversary can breach privacy further by exploiting time-dependency of semantics. We implement and evaluate a sensitivity-aware protection mechanism in this setting as well. The battle for privacy requires social awareness and will to win. However, the slow progress on the front of law and regulations… Advisors/Committee Members: Aberer, Karl.

Subjects/Keywords: location privacy; privacy concerns; location semantics; mobile applications; privacy sensitivities; inference attacks; bayesian inference; bayesian networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ağir, B. (2016). Context and Semantic Aware Location Privacy. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/219121

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):

Ağir, Berker. “Context and Semantic Aware Location Privacy.” 2016. Thesis, EPFL. Accessed September 22, 2019. http://infoscience.epfl.ch/record/219121.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ağir, Berker. “Context and Semantic Aware Location Privacy.” 2016. Web. 22 Sep 2019.

Vancouver:

Ağir B. Context and Semantic Aware Location Privacy. [Internet] [Thesis]. EPFL; 2016. [cited 2019 Sep 22]. Available from: http://infoscience.epfl.ch/record/219121.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ağir B. Context and Semantic Aware Location Privacy. [Thesis]. EPFL; 2016. Available from: http://infoscience.epfl.ch/record/219121

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations