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You searched for subject:(bayes bayesian consistency dimensional empirical g hierarchical high necessary posterior prior regression sufficient zellner). Showing records 1 – 30 of 51173 total matches.

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University of Florida

1. Sparks, Douglas Kyle. Posterior Consistency of Bayesian Regression Models.

Degree: PhD, Statistics, 2012, University of Florida

 We develop conditions for posterior consistency under a variety of Bayesian regression models, many of which are both necessary and sufficient.  We allow the number… (more)

Subjects/Keywords: Bayesian networks; Density; Frequentism; Mathematical independent variables; Multilevel models; Random variables; Regression analysis; Sample size; Statistics; Sufficient conditions; bayes  – bayesian  – consistency  – dimensional  – empirical  – g  – hierarchical  – high  – necessary  – posterior  – prior  – regression  – sufficient  – zellner

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

APA (6th Edition):

Sparks, D. K. (2012). Posterior Consistency of Bayesian Regression Models. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044654

Chicago Manual of Style (16th Edition):

Sparks, Douglas Kyle. “Posterior Consistency of Bayesian Regression Models.” 2012. Doctoral Dissertation, University of Florida. Accessed September 19, 2019. http://ufdc.ufl.edu/UFE0044654.

MLA Handbook (7th Edition):

Sparks, Douglas Kyle. “Posterior Consistency of Bayesian Regression Models.” 2012. Web. 19 Sep 2019.

Vancouver:

Sparks DK. Posterior Consistency of Bayesian Regression Models. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2019 Sep 19]. Available from: http://ufdc.ufl.edu/UFE0044654.

Council of Science Editors:

Sparks DK. Posterior Consistency of Bayesian Regression Models. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044654


University of Illinois – Urbana-Champaign

2. Yang, Yunwen. Bayesian empirical likelihood for quantile regression.

Degree: PhD, 0329, 2012, University of Illinois – Urbana-Champaign

Bayesian inference provides a flexible way of combiningg data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian(more)

Subjects/Keywords: Efficiency; Empirical likelihood; High quantiles; Quantile regression; Prior; Posterior.

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

APA (6th Edition):

Yang, Y. (2012). Bayesian empirical likelihood for quantile regression. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/29522

Chicago Manual of Style (16th Edition):

Yang, Yunwen. “Bayesian empirical likelihood for quantile regression.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed September 19, 2019. http://hdl.handle.net/2142/29522.

MLA Handbook (7th Edition):

Yang, Yunwen. “Bayesian empirical likelihood for quantile regression.” 2012. Web. 19 Sep 2019.

Vancouver:

Yang Y. Bayesian empirical likelihood for quantile regression. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2142/29522.

Council of Science Editors:

Yang Y. Bayesian empirical likelihood for quantile regression. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/29522


University of Cincinnati

3. SARTOR, MAUREEN A. TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS.

Degree: PhD, Medicine : Biostatistics (Environmental Health), 2007, University of Cincinnati

 DNA microarrays are a revolutionary technology able to measure the expression levels of thousands of genes simultaneously, providing a snapshot in time of a tissue… (more)

Subjects/Keywords: microarray; empirical Bayes; hierarchical Bayesian model; splines; gene set enrichment analysis; microarray data analysis; posterior predictive p-values

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APA (6th Edition):

SARTOR, M. A. (2007). TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673

Chicago Manual of Style (16th Edition):

SARTOR, MAUREEN A. “TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS.” 2007. Doctoral Dissertation, University of Cincinnati. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.

MLA Handbook (7th Edition):

SARTOR, MAUREEN A. “TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS.” 2007. Web. 19 Sep 2019.

Vancouver:

SARTOR MA. TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS. [Internet] [Doctoral dissertation]. University of Cincinnati; 2007. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.

Council of Science Editors:

SARTOR MA. TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS. [Doctoral Dissertation]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673


EPFL

4. Leboucq, Alix. Meta-analysis of Incomplete Microarray Studies.

Degree: 2014, EPFL

 Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information, providing only… (more)

Subjects/Keywords: clustering; empirical Bayes estimation; hierarchical Bayesian model; high-dimensional data; large covariance matrix estimation; MCMC; meta-analysis; microarray gene expression data; modules

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APA (6th Edition):

Leboucq, A. (2014). Meta-analysis of Incomplete Microarray Studies. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/202163

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

Chicago Manual of Style (16th Edition):

Leboucq, Alix. “Meta-analysis of Incomplete Microarray Studies.” 2014. Thesis, EPFL. Accessed September 19, 2019. http://infoscience.epfl.ch/record/202163.

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

MLA Handbook (7th Edition):

Leboucq, Alix. “Meta-analysis of Incomplete Microarray Studies.” 2014. Web. 19 Sep 2019.

Vancouver:

Leboucq A. Meta-analysis of Incomplete Microarray Studies. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Sep 19]. Available from: http://infoscience.epfl.ch/record/202163.

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

Council of Science Editors:

Leboucq A. Meta-analysis of Incomplete Microarray Studies. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/202163

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


University of Florida

5. Buta, Eugenia. Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis.

Degree: PhD, Statistics, 2010, University of Florida

 Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis We consider situations in Bayesian analysis where we have a family of priors on the… (more)

Subjects/Keywords: Bayes estimators; Ergodic theory; Estimation methods; Estimators; Markov chains; Maximum likelihood estimations; Modeling; Point estimators; Skeleton; Statistics; analysis, bayes, bayesian, empirical, factor, hyperparameter, posterior, prior, selection, sensitivity, variable

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

APA (6th Edition):

Buta, E. (2010). Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0041145

Chicago Manual of Style (16th Edition):

Buta, Eugenia. “Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis.” 2010. Doctoral Dissertation, University of Florida. Accessed September 19, 2019. http://ufdc.ufl.edu/UFE0041145.

MLA Handbook (7th Edition):

Buta, Eugenia. “Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis.” 2010. Web. 19 Sep 2019.

Vancouver:

Buta E. Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis. [Internet] [Doctoral dissertation]. University of Florida; 2010. [cited 2019 Sep 19]. Available from: http://ufdc.ufl.edu/UFE0041145.

Council of Science Editors:

Buta E. Computational Approaches for Empirical Bayes Methods and Bayesian Sensitivity Analysis. [Doctoral Dissertation]. University of Florida; 2010. Available from: http://ufdc.ufl.edu/UFE0041145


University of Florida

6. Dasgupta, Shibasish. High Dimensional Inference and Variable Selection.

Degree: PhD, Statistics, 2013, University of Florida

 This dissertation consists of three research projects. The first project focuses on asymptotic expansions of posteriors for generalized linear models (GLM) with canonical link functions… (more)

Subjects/Keywords: Estimators; Generalized linear model; Linear models; Linear regression; Maximum likelihood estimations; Oracles; Regression analysis; Sample size; Statistical models; Statistics; asymptotic  – bayesian  – consistency  – dimensional  – expansion  – glm  – high  – inference  – kullback  – lasso  – leibler  – normality  – oracle  – posterior  – property  – selection  – variable

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

APA (6th Edition):

Dasgupta, S. (2013). High Dimensional Inference and Variable Selection. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045721

Chicago Manual of Style (16th Edition):

Dasgupta, Shibasish. “High Dimensional Inference and Variable Selection.” 2013. Doctoral Dissertation, University of Florida. Accessed September 19, 2019. http://ufdc.ufl.edu/UFE0045721.

MLA Handbook (7th Edition):

Dasgupta, Shibasish. “High Dimensional Inference and Variable Selection.” 2013. Web. 19 Sep 2019.

Vancouver:

Dasgupta S. High Dimensional Inference and Variable Selection. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2019 Sep 19]. Available from: http://ufdc.ufl.edu/UFE0045721.

Council of Science Editors:

Dasgupta S. High Dimensional Inference and Variable Selection. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045721


University of Cincinnati

7. Glore, Mary Lee. The Threshold Prior in Bayesian Hypothesis Testing.

Degree: PhD, Arts and Sciences: Mathematics (Statistics), 2014, University of Cincinnati

Bayesian hypothesis testing provides an attractive alternative that overcomes the difficulties with interpreting the p-value or calibrating it. Bayesian hypothesis testing also provides measures of… (more)

Subjects/Keywords: Statistics; Bayes; regression; Mathematica; nonlocal; g-prior; time series

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APA (6th Edition):

Glore, M. L. (2014). The Threshold Prior in Bayesian Hypothesis Testing. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546

Chicago Manual of Style (16th Edition):

Glore, Mary Lee. “The Threshold Prior in Bayesian Hypothesis Testing.” 2014. Doctoral Dissertation, University of Cincinnati. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546.

MLA Handbook (7th Edition):

Glore, Mary Lee. “The Threshold Prior in Bayesian Hypothesis Testing.” 2014. Web. 19 Sep 2019.

Vancouver:

Glore ML. The Threshold Prior in Bayesian Hypothesis Testing. [Internet] [Doctoral dissertation]. University of Cincinnati; 2014. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546.

Council of Science Editors:

Glore ML. The Threshold Prior in Bayesian Hypothesis Testing. [Doctoral Dissertation]. University of Cincinnati; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1416570546


Texas A&M University

8. Song, Qifan. Variable Selection for Ultra High Dimensional Data.

Degree: 2014, Texas A&M University

 Variable selection plays an important role for the high dimensional data analysis. In this work, we first propose a Bayesian variable selection approach for ultra-high(more)

Subjects/Keywords: High Dimensional Variable Selection; Big Data; Penalized Likelihood Approach; Posterior Consistency

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APA (6th Edition):

Song, Q. (2014). Variable Selection for Ultra High Dimensional Data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153224

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

Chicago Manual of Style (16th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Thesis, Texas A&M University. Accessed September 19, 2019. http://hdl.handle.net/1969.1/153224.

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

MLA Handbook (7th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Web. 19 Sep 2019.

Vancouver:

Song Q. Variable Selection for Ultra High Dimensional Data. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1969.1/153224.

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

Council of Science Editors:

Song Q. Variable Selection for Ultra High Dimensional Data. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153224

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

9. Grollemund, Paul-Marie. Régression linéaire bayésienne sur données fonctionnelles : Functional Bayesian linear regression.

Degree: Docteur es, Biostatistique, 2017, Montpellier

Un outil fondamental en statistique est le modèle de régression linéaire. Lorsqu'une des covariables est une fonction, on fait face à un problème de statistique… (more)

Subjects/Keywords: Régression linéaire fonctionnelle; Bayésien; Parcimonie; Consistance; Elicitation; Functional linear regression; Bayesian; Sparsity; Posterior consistency; Elicitation

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APA (6th Edition):

Grollemund, P. (2017). Régression linéaire bayésienne sur données fonctionnelles : Functional Bayesian linear regression. (Doctoral Dissertation). Montpellier. Retrieved from http://www.theses.fr/2017MONTS045

Chicago Manual of Style (16th Edition):

Grollemund, Paul-Marie. “Régression linéaire bayésienne sur données fonctionnelles : Functional Bayesian linear regression.” 2017. Doctoral Dissertation, Montpellier. Accessed September 19, 2019. http://www.theses.fr/2017MONTS045.

MLA Handbook (7th Edition):

Grollemund, Paul-Marie. “Régression linéaire bayésienne sur données fonctionnelles : Functional Bayesian linear regression.” 2017. Web. 19 Sep 2019.

Vancouver:

Grollemund P. Régression linéaire bayésienne sur données fonctionnelles : Functional Bayesian linear regression. [Internet] [Doctoral dissertation]. Montpellier; 2017. [cited 2019 Sep 19]. Available from: http://www.theses.fr/2017MONTS045.

Council of Science Editors:

Grollemund P. Régression linéaire bayésienne sur données fonctionnelles : Functional Bayesian linear regression. [Doctoral Dissertation]. Montpellier; 2017. Available from: http://www.theses.fr/2017MONTS045


University of Central Florida

10. Dennis, Dana-Marie. Chemical Analysis, Databasing, and Statistical Analysis of Smokeless Powders for Forensic Application.

Degree: 2015, University of Central Florida

 Smokeless powders are a set of energetic materials, known as low explosives, which are typically utilized for reloading ammunition. There are three types which differ… (more)

Subjects/Keywords: Smokeless powders; agglomerative hierarchical cluster analysis; total ion spectrum; the intense ion rule; k nearest neighbors; linear discriminant analysis; quadratic discriminant analysis; bayes* theorem; bayesian networks; prior probability; posterior probability; Chemistry; Dissertations, Academic  – Sciences; Sciences  – Dissertations, Academic

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APA (6th Edition):

Dennis, D. (2015). Chemical Analysis, Databasing, and Statistical Analysis of Smokeless Powders for Forensic Application. (Doctoral Dissertation). University of Central Florida. Retrieved from https://stars.library.ucf.edu/etd/1212

Chicago Manual of Style (16th Edition):

Dennis, Dana-Marie. “Chemical Analysis, Databasing, and Statistical Analysis of Smokeless Powders for Forensic Application.” 2015. Doctoral Dissertation, University of Central Florida. Accessed September 19, 2019. https://stars.library.ucf.edu/etd/1212.

MLA Handbook (7th Edition):

Dennis, Dana-Marie. “Chemical Analysis, Databasing, and Statistical Analysis of Smokeless Powders for Forensic Application.” 2015. Web. 19 Sep 2019.

Vancouver:

Dennis D. Chemical Analysis, Databasing, and Statistical Analysis of Smokeless Powders for Forensic Application. [Internet] [Doctoral dissertation]. University of Central Florida; 2015. [cited 2019 Sep 19]. Available from: https://stars.library.ucf.edu/etd/1212.

Council of Science Editors:

Dennis D. Chemical Analysis, Databasing, and Statistical Analysis of Smokeless Powders for Forensic Application. [Doctoral Dissertation]. University of Central Florida; 2015. Available from: https://stars.library.ucf.edu/etd/1212

11. Baragatti, Meïli. Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance : Trust, social cohesion : conceptual issues, indicators and economic effects.

Degree: Docteur es, Mathématiques, 2011, Aix-Marseille 2

 Cette thèse se décompose en deux parties. Dans un premier temps nous nous intéressons à la sélection bayésienne de variables dans un modèle probit mixte.L'objectif… (more)

Subjects/Keywords: Sélection bayésienne de variables; Modèle probit mixte; A priori de Zellner; Paramètre ridge; Monte Carlo Markov Chains; Parallel Tempering; Equi-Energy Sampler; Approximate Bayesian Computation; Méthodes sans vraisemblance; Bayesian variable selection; Probit mixed model; Zellner g-prior; Ridge parameter; Monte Carlo Markov Chains; Parallel Tempering; Equi-Energy Sampler; Approximate Bayesian Computation; Likelihood-Free methods

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APA (6th Edition):

Baragatti, M. (2011). Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance : Trust, social cohesion : conceptual issues, indicators and economic effects. (Doctoral Dissertation). Aix-Marseille 2. Retrieved from http://www.theses.fr/2011AIX22100

Chicago Manual of Style (16th Edition):

Baragatti, Meïli. “Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance : Trust, social cohesion : conceptual issues, indicators and economic effects.” 2011. Doctoral Dissertation, Aix-Marseille 2. Accessed September 19, 2019. http://www.theses.fr/2011AIX22100.

MLA Handbook (7th Edition):

Baragatti, Meïli. “Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance : Trust, social cohesion : conceptual issues, indicators and economic effects.” 2011. Web. 19 Sep 2019.

Vancouver:

Baragatti M. Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance : Trust, social cohesion : conceptual issues, indicators and economic effects. [Internet] [Doctoral dissertation]. Aix-Marseille 2; 2011. [cited 2019 Sep 19]. Available from: http://www.theses.fr/2011AIX22100.

Council of Science Editors:

Baragatti M. Sélection bayésienne de variables et méthodes de type Parallel Tempering avec et sans vraisemblance : Trust, social cohesion : conceptual issues, indicators and economic effects. [Doctoral Dissertation]. Aix-Marseille 2; 2011. Available from: http://www.theses.fr/2011AIX22100

12. Wang, Min. Bayesian Hypothesis Testing and Variable Selection in High Dimensional Regression.

Degree: PhD, Mathematical Science, 2013, Clemson University

 This dissertation consists of three distinct but related research projects. First of all, we study the Bayesian approach to model selection in the class of… (more)

Subjects/Keywords: Bayes Factor; Hypothesis Testing; Model Selection; Model Selection Consistency; Posterior Distribution; Zellner's g-prior; Statistics and Probability

Bayes factor for variable selection and hypothesis testing in high dimensional regression… …hyper-g/n prior) and also investigated the model selection consistency of the Bayes… …consistency of the Bayes factor derived based on combined use of Zellner’s g-prior for the… …6.5 The consistency regions of the proposed Bayes factor for the beta-prime prior for… …considered three families of gpriors (i.e., the Zellner-Siow prior, the hyper-g prior, and the… 

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APA (6th Edition):

Wang, M. (2013). Bayesian Hypothesis Testing and Variable Selection in High Dimensional Regression. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/1112

Chicago Manual of Style (16th Edition):

Wang, Min. “Bayesian Hypothesis Testing and Variable Selection in High Dimensional Regression.” 2013. Doctoral Dissertation, Clemson University. Accessed September 19, 2019. https://tigerprints.clemson.edu/all_dissertations/1112.

MLA Handbook (7th Edition):

Wang, Min. “Bayesian Hypothesis Testing and Variable Selection in High Dimensional Regression.” 2013. Web. 19 Sep 2019.

Vancouver:

Wang M. Bayesian Hypothesis Testing and Variable Selection in High Dimensional Regression. [Internet] [Doctoral dissertation]. Clemson University; 2013. [cited 2019 Sep 19]. Available from: https://tigerprints.clemson.edu/all_dissertations/1112.

Council of Science Editors:

Wang M. Bayesian Hypothesis Testing and Variable Selection in High Dimensional Regression. [Doctoral Dissertation]. Clemson University; 2013. Available from: https://tigerprints.clemson.edu/all_dissertations/1112


University of Rochester

13. Kim, Taeryang (1967 - ); Braun, David. In defense of a posteriori minimal physicalism.

Degree: PhD, 2011, University of Rochester

 The aim of this dissertation is to defend a version of <i>a posteriori</i> minimal physicalism which claims that everything including phenomenal consciousness supervenes globally logically… (more)

Subjects/Keywords: Supervenience; Conceivability; Two dimensional; Necessary a posterior; A priori deducibility; Humean

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APA (6th Edition):

Kim, Taeryang (1967 - ); Braun, D. (2011). In defense of a posteriori minimal physicalism. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/14826

Chicago Manual of Style (16th Edition):

Kim, Taeryang (1967 - ); Braun, David. “In defense of a posteriori minimal physicalism.” 2011. Doctoral Dissertation, University of Rochester. Accessed September 19, 2019. http://hdl.handle.net/1802/14826.

MLA Handbook (7th Edition):

Kim, Taeryang (1967 - ); Braun, David. “In defense of a posteriori minimal physicalism.” 2011. Web. 19 Sep 2019.

Vancouver:

Kim, Taeryang (1967 - ); Braun D. In defense of a posteriori minimal physicalism. [Internet] [Doctoral dissertation]. University of Rochester; 2011. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1802/14826.

Council of Science Editors:

Kim, Taeryang (1967 - ); Braun D. In defense of a posteriori minimal physicalism. [Doctoral Dissertation]. University of Rochester; 2011. Available from: http://hdl.handle.net/1802/14826


University of Louisville

14. Evans, Sara. Bayesian regression analysis.

Degree: MA, 2012, University of Louisville

Regression analysis is a statistical method used to relate a variable of interest, typically y (the dependent variable), to a set of independent variables,… (more)

Subjects/Keywords: Frequentist regression; Bayes' theorem; Bayesian regression

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APA (6th Edition):

Evans, S. (2012). Bayesian regression analysis. (Masters Thesis). University of Louisville. Retrieved from 10.18297/etd/412 ; https://ir.library.louisville.edu/etd/412

Chicago Manual of Style (16th Edition):

Evans, Sara. “Bayesian regression analysis.” 2012. Masters Thesis, University of Louisville. Accessed September 19, 2019. 10.18297/etd/412 ; https://ir.library.louisville.edu/etd/412.

MLA Handbook (7th Edition):

Evans, Sara. “Bayesian regression analysis.” 2012. Web. 19 Sep 2019.

Vancouver:

Evans S. Bayesian regression analysis. [Internet] [Masters thesis]. University of Louisville; 2012. [cited 2019 Sep 19]. Available from: 10.18297/etd/412 ; https://ir.library.louisville.edu/etd/412.

Council of Science Editors:

Evans S. Bayesian regression analysis. [Masters Thesis]. University of Louisville; 2012. Available from: 10.18297/etd/412 ; https://ir.library.louisville.edu/etd/412


University of Technology, Sydney

15. Menictas, M. Variational inference for heteroscedastic and longitudinal regression models.

Degree: 2015, University of Technology, Sydney

 The focus of this thesis is on the development and assessment of mean field variational Bayes (MFVB), which is a fast, deterministic tool for inference… (more)

Subjects/Keywords: Mean field variational Bayes (MFVB).; Bayesian hierarchical model setting.; Markov chain Monte Carlo (MCMC) benchmark.; Heteroscedastic and longitudinal semiparametric regression models.

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APA (6th Edition):

Menictas, M. (2015). Variational inference for heteroscedastic and longitudinal regression models. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/38942

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

Chicago Manual of Style (16th Edition):

Menictas, M. “Variational inference for heteroscedastic and longitudinal regression models.” 2015. Thesis, University of Technology, Sydney. Accessed September 19, 2019. http://hdl.handle.net/10453/38942.

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

MLA Handbook (7th Edition):

Menictas, M. “Variational inference for heteroscedastic and longitudinal regression models.” 2015. Web. 19 Sep 2019.

Vancouver:

Menictas M. Variational inference for heteroscedastic and longitudinal regression models. [Internet] [Thesis]. University of Technology, Sydney; 2015. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10453/38942.

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

Council of Science Editors:

Menictas M. Variational inference for heteroscedastic and longitudinal regression models. [Thesis]. University of Technology, Sydney; 2015. Available from: http://hdl.handle.net/10453/38942

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


The Ohio State University

16. Wang, Xiaomu. Robust Bayes in Hierarchical Modeling and Empirical BayesAnalysis in Multivariate Estimation.

Degree: PhD, Statistics, 2015, The Ohio State University

 With the modern development of statistical data analysis, the data volume increasesand the data dimension increases correspondingly. This thesis investigatestwo classic Bayes problems: robust Bayes(more)

Subjects/Keywords: Statistics; robust Bayes, multivariate estimation, empirical Bayes, James-Stein, hierarchical model

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APA (6th Edition):

Wang, X. (2015). Robust Bayes in Hierarchical Modeling and Empirical BayesAnalysis in Multivariate Estimation. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1449069220

Chicago Manual of Style (16th Edition):

Wang, Xiaomu. “Robust Bayes in Hierarchical Modeling and Empirical BayesAnalysis in Multivariate Estimation.” 2015. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1449069220.

MLA Handbook (7th Edition):

Wang, Xiaomu. “Robust Bayes in Hierarchical Modeling and Empirical BayesAnalysis in Multivariate Estimation.” 2015. Web. 19 Sep 2019.

Vancouver:

Wang X. Robust Bayes in Hierarchical Modeling and Empirical BayesAnalysis in Multivariate Estimation. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1449069220.

Council of Science Editors:

Wang X. Robust Bayes in Hierarchical Modeling and Empirical BayesAnalysis in Multivariate Estimation. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1449069220

17. Lee, Wayne Tai. Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure.

Degree: Statistics, 2013, University of California – Berkeley

 This dissertation is a compilation of three different applied statistical problems from the Bayesian perspective. Although the statistical question in each problem is different, a… (more)

Subjects/Keywords: Statistics; Bayesian Statistics; Global Extreme; Hierarchical Multilabel Classification; High Dimensional; Spatial Statistics; Surface Wind

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APA (6th Edition):

Lee, W. T. (2013). Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/1mp792b6

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

Chicago Manual of Style (16th Edition):

Lee, Wayne Tai. “Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure.” 2013. Thesis, University of California – Berkeley. Accessed September 19, 2019. http://www.escholarship.org/uc/item/1mp792b6.

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

MLA Handbook (7th Edition):

Lee, Wayne Tai. “Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure.” 2013. Web. 19 Sep 2019.

Vancouver:

Lee WT. Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2019 Sep 19]. Available from: http://www.escholarship.org/uc/item/1mp792b6.

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

Council of Science Editors:

Lee WT. Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/1mp792b6

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


University of Newcastle

18. Tuyl, Frank Adrianus Wilhelmus Maria. Estimation of the Binomial parameter: in defence of Bayes (1763).

Degree: PhD, 2007, University of Newcastle

Research Doctorate - Doctor of Philosophy (PhD)

Interval estimation of the Binomial parameter è, representing the true probability of a success, is a problem of… (more)

Subjects/Keywords: Bayesian inference; binomial distribution; relative risk; Rule of Three; zero events; prior families; posterior predictive; Bayes-Laplace prior; invariance; Jeffreys prior; noninformative prior; reference prior; uniform prior; highest posterior density; odds ratio

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APA (6th Edition):

Tuyl, F. A. W. M. (2007). Estimation of the Binomial parameter: in defence of Bayes (1763). (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/25730

Chicago Manual of Style (16th Edition):

Tuyl, Frank Adrianus Wilhelmus Maria. “Estimation of the Binomial parameter: in defence of Bayes (1763).” 2007. Doctoral Dissertation, University of Newcastle. Accessed September 19, 2019. http://hdl.handle.net/1959.13/25730.

MLA Handbook (7th Edition):

Tuyl, Frank Adrianus Wilhelmus Maria. “Estimation of the Binomial parameter: in defence of Bayes (1763).” 2007. Web. 19 Sep 2019.

Vancouver:

Tuyl FAWM. Estimation of the Binomial parameter: in defence of Bayes (1763). [Internet] [Doctoral dissertation]. University of Newcastle; 2007. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1959.13/25730.

Council of Science Editors:

Tuyl FAWM. Estimation of the Binomial parameter: in defence of Bayes (1763). [Doctoral Dissertation]. University of Newcastle; 2007. Available from: http://hdl.handle.net/1959.13/25730

19. Li, Qiong. Bayesian Estimation of Graphical Gaussian Models with Edges and Vertices Symmetries.

Degree: PhD, Mathematics & Statistics, 2017, York University

 We consider the Bayesian analysis of undirected graphical Gaussian models with edges and vertices symmetries. The graphical Gaussian models with equality constraints on the precision… (more)

Subjects/Keywords: Statistics; Asymptotic normality; Bayesian estimator; Colored G-Wishart distribution; Conditional independence; Conjugate prior; Consistency; Marginal model; Metropolis-Hastings; Large deviation; Symmetry constraint.

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APA (6th Edition):

Li, Q. (2017). Bayesian Estimation of Graphical Gaussian Models with Edges and Vertices Symmetries. (Doctoral Dissertation). York University. Retrieved from http://hdl.handle.net/10315/33452

Chicago Manual of Style (16th Edition):

Li, Qiong. “Bayesian Estimation of Graphical Gaussian Models with Edges and Vertices Symmetries.” 2017. Doctoral Dissertation, York University. Accessed September 19, 2019. http://hdl.handle.net/10315/33452.

MLA Handbook (7th Edition):

Li, Qiong. “Bayesian Estimation of Graphical Gaussian Models with Edges and Vertices Symmetries.” 2017. Web. 19 Sep 2019.

Vancouver:

Li Q. Bayesian Estimation of Graphical Gaussian Models with Edges and Vertices Symmetries. [Internet] [Doctoral dissertation]. York University; 2017. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10315/33452.

Council of Science Editors:

Li Q. Bayesian Estimation of Graphical Gaussian Models with Edges and Vertices Symmetries. [Doctoral Dissertation]. York University; 2017. Available from: http://hdl.handle.net/10315/33452


University of Louisville

20. Yang, Dake. Consistency of differentially expressed gene rankings based on subsets of microarray data.

Degree: MS, 2011, University of Louisville

  Data derived from gene expression microarrays are frequently used to identify candidate genes which can characterize and distinguish between two biological phenotypes. A key… (more)

Subjects/Keywords: Microarray; Empirical Bayes; Differentially expressed; Consistency; T-test; SAM

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APA (6th Edition):

Yang, D. (2011). Consistency of differentially expressed gene rankings based on subsets of microarray data. (Masters Thesis). University of Louisville. Retrieved from 10.18297/etd/1615 ; https://ir.library.louisville.edu/etd/1615

Chicago Manual of Style (16th Edition):

Yang, Dake. “Consistency of differentially expressed gene rankings based on subsets of microarray data.” 2011. Masters Thesis, University of Louisville. Accessed September 19, 2019. 10.18297/etd/1615 ; https://ir.library.louisville.edu/etd/1615.

MLA Handbook (7th Edition):

Yang, Dake. “Consistency of differentially expressed gene rankings based on subsets of microarray data.” 2011. Web. 19 Sep 2019.

Vancouver:

Yang D. Consistency of differentially expressed gene rankings based on subsets of microarray data. [Internet] [Masters thesis]. University of Louisville; 2011. [cited 2019 Sep 19]. Available from: 10.18297/etd/1615 ; https://ir.library.louisville.edu/etd/1615.

Council of Science Editors:

Yang D. Consistency of differentially expressed gene rankings based on subsets of microarray data. [Masters Thesis]. University of Louisville; 2011. Available from: 10.18297/etd/1615 ; https://ir.library.louisville.edu/etd/1615

21. Chang, Chao. Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.

Degree: PhD, Mathematics, 2015, Washington University in St. Louis

  We propose new nonparametric Bayesian approaches to quantile regression using Dirichlet process mixture (DPM) models. All the existing quantile regression methods based on DPMs… (more)

Subjects/Keywords: Dirichlet Process Mixture, Nonparametric Bayesian, Posterior Consistency, Quantile Regression; Mathematics

…117] proposed to solve the quantile regression problem using Bayesian empirical… …proposed an approach to quantile regression based on the Bayesian exponentially tilted empirical… …establish the theoretical guarantee of the posterior consistency on the regression coefficients… …Currently, there is no theory for the posterior consistency in nonparametric Bayesian quantile… …empirical Bayes method. Note that for the prior of σi ’s, they all have mean equal to d. We want… 

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

APA (6th Edition):

Chang, C. (2015). Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/art_sci_etds/458

Chicago Manual of Style (16th Edition):

Chang, Chao. “Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.” 2015. Doctoral Dissertation, Washington University in St. Louis. Accessed September 19, 2019. https://openscholarship.wustl.edu/art_sci_etds/458.

MLA Handbook (7th Edition):

Chang, Chao. “Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models.” 2015. Web. 19 Sep 2019.

Vancouver:

Chang C. Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2015. [cited 2019 Sep 19]. Available from: https://openscholarship.wustl.edu/art_sci_etds/458.

Council of Science Editors:

Chang C. Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models. [Doctoral Dissertation]. Washington University in St. Louis; 2015. Available from: https://openscholarship.wustl.edu/art_sci_etds/458


Bowling Green State University

22. Dong, Fanglong. Bayesian Model Checking in Multivariate Discrete Regression Problems.

Degree: PhD, Mathematics/Probability and Statistics, 2008, Bowling Green State University

 Ordinal data are common in the academic area such as a student grade, A, B, C, D, orF, also ordinal data are common in other… (more)

Subjects/Keywords: Statistics; Bayesian statistics; ordinal data; bayes factor; deviance; posterior distribution

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APA (6th Edition):

Dong, F. (2008). Bayesian Model Checking in Multivariate Discrete Regression Problems. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1223329230

Chicago Manual of Style (16th Edition):

Dong, Fanglong. “Bayesian Model Checking in Multivariate Discrete Regression Problems.” 2008. Doctoral Dissertation, Bowling Green State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1223329230.

MLA Handbook (7th Edition):

Dong, Fanglong. “Bayesian Model Checking in Multivariate Discrete Regression Problems.” 2008. Web. 19 Sep 2019.

Vancouver:

Dong F. Bayesian Model Checking in Multivariate Discrete Regression Problems. [Internet] [Doctoral dissertation]. Bowling Green State University; 2008. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1223329230.

Council of Science Editors:

Dong F. Bayesian Model Checking in Multivariate Discrete Regression Problems. [Doctoral Dissertation]. Bowling Green State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1223329230


University of Texas – Austin

23. Wang, Mengjie. Assigning g in Zellner's g prior for Bayesian variable selection.

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

 There are numerous frequentist statistics variable selection methods such as Stepwise regression, AIC and BIC etc. In particular, the latter two criteria include a penalty… (more)

Subjects/Keywords: Model selection; Bayes factor; BIC; Zellner's g prior; Type I error

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APA (6th Edition):

Wang, M. (2015). Assigning g in Zellner's g prior for Bayesian variable selection. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32496

Chicago Manual of Style (16th Edition):

Wang, Mengjie. “Assigning g in Zellner's g prior for Bayesian variable selection.” 2015. Masters Thesis, University of Texas – Austin. Accessed September 19, 2019. http://hdl.handle.net/2152/32496.

MLA Handbook (7th Edition):

Wang, Mengjie. “Assigning g in Zellner's g prior for Bayesian variable selection.” 2015. Web. 19 Sep 2019.

Vancouver:

Wang M. Assigning g in Zellner's g prior for Bayesian variable selection. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2152/32496.

Council of Science Editors:

Wang M. Assigning g in Zellner's g prior for Bayesian variable selection. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32496


Kansas State University

24. Anderson, Michael P. Bayesian classification of DNA barcodes.

Degree: PhD, Department of Statistics, 2009, Kansas State University

 DNA barcodes are short strands of nucleotide bases taken from the cytochrome c oxidase subunit 1 (COI) of the mitochondrial DNA (mtDNA). A single barcode… (more)

Subjects/Keywords: DNA Barcodes; Bayesian Classification; Species Discovery; Naive Bayes Classifier; Sequential Analysis; High-dimensional Data; Statistics (0463)

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APA (6th Edition):

Anderson, M. P. (2009). Bayesian classification of DNA barcodes. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/2247

Chicago Manual of Style (16th Edition):

Anderson, Michael P. “Bayesian classification of DNA barcodes.” 2009. Doctoral Dissertation, Kansas State University. Accessed September 19, 2019. http://hdl.handle.net/2097/2247.

MLA Handbook (7th Edition):

Anderson, Michael P. “Bayesian classification of DNA barcodes.” 2009. Web. 19 Sep 2019.

Vancouver:

Anderson MP. Bayesian classification of DNA barcodes. [Internet] [Doctoral dissertation]. Kansas State University; 2009. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2097/2247.

Council of Science Editors:

Anderson MP. Bayesian classification of DNA barcodes. [Doctoral Dissertation]. Kansas State University; 2009. Available from: http://hdl.handle.net/2097/2247


The Ohio State University

25. zheng, jiayin. Calibrated Bayes Factor and Bayesian Model Averaging.

Degree: PhD, Statistics, 2018, The Ohio State University

 There is a rich history of work on model selection and averaging in the statisticsliterature. The Bayesian paradigm provides an approach to model selection whichsuccessfully… (more)

Subjects/Keywords: Statistics; model selection, Bayes factor, unit information prior, Bayesian model averaging

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APA (6th Edition):

zheng, j. (2018). Calibrated Bayes Factor and Bayesian Model Averaging. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265

Chicago Manual of Style (16th Edition):

zheng, jiayin. “Calibrated Bayes Factor and Bayesian Model Averaging.” 2018. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265.

MLA Handbook (7th Edition):

zheng, jiayin. “Calibrated Bayes Factor and Bayesian Model Averaging.” 2018. Web. 19 Sep 2019.

Vancouver:

zheng j. Calibrated Bayes Factor and Bayesian Model Averaging. [Internet] [Doctoral dissertation]. The Ohio State University; 2018. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265.

Council of Science Editors:

zheng j. Calibrated Bayes Factor and Bayesian Model Averaging. [Doctoral Dissertation]. The Ohio State University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265


University of New South Wales

26. Rodrigues, Guilherme. New methods for infinite and high-dimensional approximate Bayesian computation.

Degree: Mathematics & Statistics, 2017, University of New South Wales

 The remarkable complexity of modern applied problems often requires the use of probabilistic models where the likelihood is intractable  – in the sense that it… (more)

Subjects/Keywords: Gibbs sampler; approximate Bayesian computation (ABC); Gaussian process prior; hierarchical models; indirect inferenceintractable state space models; likelihood-free inference; nonparametric density estimation; regression-adjustment

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APA (6th Edition):

Rodrigues, G. (2017). New methods for infinite and high-dimensional approximate Bayesian computation. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Rodrigues, Guilherme. “New methods for infinite and high-dimensional approximate Bayesian computation.” 2017. Doctoral Dissertation, University of New South Wales. Accessed September 19, 2019. http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true.

MLA Handbook (7th Edition):

Rodrigues, Guilherme. “New methods for infinite and high-dimensional approximate Bayesian computation.” 2017. Web. 19 Sep 2019.

Vancouver:

Rodrigues G. New methods for infinite and high-dimensional approximate Bayesian computation. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Sep 19]. Available from: http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true.

Council of Science Editors:

Rodrigues G. New methods for infinite and high-dimensional approximate Bayesian computation. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58630 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:46480/SOURCE02?view=true


Duke University

27. Qamar, Shaan. Topics in Modern Bayesian Computation .

Degree: 2015, Duke University

  Collections of large volumes of rich and complex data has become ubiquitous in recent years, posing new challenges in methodological and theoretical statistics alike.… (more)

Subjects/Keywords: Statistics; Approximate Bayesian computation; High dimensional regression; Nonparametric regression; Scalable Markov chain Monte Carlo; Structured additive models; Variable selection

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APA (6th Edition):

Qamar, S. (2015). Topics in Modern Bayesian Computation . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/10481

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

Chicago Manual of Style (16th Edition):

Qamar, Shaan. “Topics in Modern Bayesian Computation .” 2015. Thesis, Duke University. Accessed September 19, 2019. http://hdl.handle.net/10161/10481.

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

MLA Handbook (7th Edition):

Qamar, Shaan. “Topics in Modern Bayesian Computation .” 2015. Web. 19 Sep 2019.

Vancouver:

Qamar S. Topics in Modern Bayesian Computation . [Internet] [Thesis]. Duke University; 2015. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10161/10481.

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

Council of Science Editors:

Qamar S. Topics in Modern Bayesian Computation . [Thesis]. Duke University; 2015. Available from: http://hdl.handle.net/10161/10481

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


University of Pennsylvania

28. Fuki, Igar. Bayesian Aspects of Classification Procedures.

Degree: 2013, University of Pennsylvania

 We consider several statistical approaches to binary classification and multiple hypothesis testing problems. Situations in which a binary choice must be made are common in… (more)

Subjects/Keywords: Classification procedures; empirical Bayes; False discovery rate; nonparametric mixture prior; Statistics and Probability

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APA (6th Edition):

Fuki, I. (2013). Bayesian Aspects of Classification Procedures. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/863

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

Chicago Manual of Style (16th Edition):

Fuki, Igar. “Bayesian Aspects of Classification Procedures.” 2013. Thesis, University of Pennsylvania. Accessed September 19, 2019. https://repository.upenn.edu/edissertations/863.

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

MLA Handbook (7th Edition):

Fuki, Igar. “Bayesian Aspects of Classification Procedures.” 2013. Web. 19 Sep 2019.

Vancouver:

Fuki I. Bayesian Aspects of Classification Procedures. [Internet] [Thesis]. University of Pennsylvania; 2013. [cited 2019 Sep 19]. Available from: https://repository.upenn.edu/edissertations/863.

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

Council of Science Editors:

Fuki I. Bayesian Aspects of Classification Procedures. [Thesis]. University of Pennsylvania; 2013. Available from: https://repository.upenn.edu/edissertations/863

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


Brigham Young University

29. Shepherd, Brent A. Predicting Maximal Oxygen Consumption (VO2max) Levels in Adolescents.

Degree: MS, 2012, Brigham Young University

 Maximal oxygen consumption (VO2max) is considered by many to be the best overall measure of an individual's cardiovascular health. Collecting the measurement, however, requires subjecting… (more)

Subjects/Keywords: VO2max; MCMC; Bayesian Hierarchical Models; Bayes Methods; Statistics and Probability

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APA (6th Edition):

Shepherd, B. A. (2012). Predicting Maximal Oxygen Consumption (VO2max) Levels in Adolescents. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3996&context=etd

Chicago Manual of Style (16th Edition):

Shepherd, Brent A. “Predicting Maximal Oxygen Consumption (VO2max) Levels in Adolescents.” 2012. Masters Thesis, Brigham Young University. Accessed September 19, 2019. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3996&context=etd.

MLA Handbook (7th Edition):

Shepherd, Brent A. “Predicting Maximal Oxygen Consumption (VO2max) Levels in Adolescents.” 2012. Web. 19 Sep 2019.

Vancouver:

Shepherd BA. Predicting Maximal Oxygen Consumption (VO2max) Levels in Adolescents. [Internet] [Masters thesis]. Brigham Young University; 2012. [cited 2019 Sep 19]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3996&context=etd.

Council of Science Editors:

Shepherd BA. Predicting Maximal Oxygen Consumption (VO2max) Levels in Adolescents. [Masters Thesis]. Brigham Young University; 2012. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3996&context=etd


Washington University in St. Louis

30. Womack, Andrew. Predictive Alternatives in Bayesian Model Selection.

Degree: PhD, Mathematics, 2011, Washington University in St. Louis

 Model comparison and hypothesis testing is an integral part of all data analyses. In this thesis, I present two new families of information criteria that… (more)

Subjects/Keywords: Statistics; Bayes' Factor, Bayesian model selection, Information Criteria, Posterior Predictive, Renyi Entropy

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APA (6th Edition):

Womack, A. (2011). Predictive Alternatives in Bayesian Model Selection. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/etd/381

Chicago Manual of Style (16th Edition):

Womack, Andrew. “Predictive Alternatives in Bayesian Model Selection.” 2011. Doctoral Dissertation, Washington University in St. Louis. Accessed September 19, 2019. https://openscholarship.wustl.edu/etd/381.

MLA Handbook (7th Edition):

Womack, Andrew. “Predictive Alternatives in Bayesian Model Selection.” 2011. Web. 19 Sep 2019.

Vancouver:

Womack A. Predictive Alternatives in Bayesian Model Selection. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2011. [cited 2019 Sep 19]. Available from: https://openscholarship.wustl.edu/etd/381.

Council of Science Editors:

Womack A. Predictive Alternatives in Bayesian Model Selection. [Doctoral Dissertation]. Washington University in St. Louis; 2011. Available from: https://openscholarship.wustl.edu/etd/381

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