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1.
A Bayesian Hierarchical Model for Multiple Comparisons in Mixed Models
by Li, Qie, Ph.D.  Bowling Green State University. 2012: 127 pages; 10631144.
2.
Objective Bayesian inference for stress-strength models and Bayesian ANOVA
by Min, Xiaoyi, Ph.D.  University of Missouri - Columbia. 2012: 128 pages; 3537873.
3.
Networks of mixture blocks for non parametric Bayesian models with applications
by Porteous, Ian, Ph.D.  University of California, Irvine. 2010: 123 pages; 3403449.
5.
Application of systems factorial technology and hierarchical Bayesian modeling to chunking in Working Memory
by Thiele, Jonathan, Ph.D.  University of Missouri - Columbia. 2015: 126 pages; 10178999.
6.
Hierarchical Bayes estimation and empirical best prediction of small-area proportions
by Liu, Benmei, Ph.D.  University of Maryland, College Park. 2009: 223 pages; 3359401.
7.
Bayesian mortgage default models
by Xu, Feng, Ph.D.  The George Washington University. 2008: 157 pages; 3316109.
8.
Bayesian Modeling for fMRI Brain Activation and Connectivity
by Yu, Zhe, Ph.D.  University of California, Irvine. 2015: 155 pages; 3717133.
9.
Hierarchical Bayesian Model for AK Composite Estimators in the Current Population Survey (CPS)
by Li, Yuan, Ph.D.  The George Washington University. 2018: 137 pages; 10748002.
10.
Nonparametric Bayes: Inference under nonignorable missingness and model selection
by Linero, Antonio, Ph.D.  University of Florida. 2015: 147 pages; 10299009.
11.
Bayesian generative modeling for complex dynamical systems
by Guan, Jinyan, Ph.D.  The University of Arizona. 2016: 120 pages; 10109036.
13.
Estimability of Time-Varying Natural Mortality in Groundfishes: Covariates and Hierarchical Models
by Ganz, Philip D., M.S.  University of Alaska Fairbanks. 2017: 62 pages; 10603964.
14.
Small Area Estimation in a Survey of Governments
by Dumbacher, Brian Arthur, Ph.D.  The George Washington University. 2016: 130 pages; 10075469.
15.
General Purpose MCMC Sampling for Bayesian Model Averaging
by Boyles, Levi Beinarauskas, Ph.D.  University of California, Irvine. 2014: 129 pages; 3631086.
16.
Essays in the Dynamics Bayesian Models in Marketing
by Kim, Bumsoo, Ph.D.  The George Washington University. 2013: 100 pages; 3591693.
18.
Analysis of experiments to validate computer models with binary output
by Chapin, Patrick Samuel, Ph.D.  Iowa State University. 2009: 172 pages; 3389285.
19.
Semiparametric Bayesian modeling of density dependence
by Sugeno, Masatoshi, Ph.D.  State University of New York at Stony Brook. 2012: 110 pages; 3517599.
21.
Statistical inference procedure for step-stress accelerated life testing models
by Lee, Jinsuk, Ph.D.  Arizona State University. 2008: 113 pages; 3338480.
22.
Structured hierarchical models for neurons in the early visual system
by Vintch, Brett, Ph.D.  New York University. 2013: 145 pages; 3591363.
23.
Macroeconometrics with Bayesian estimation of dynamic stochastic general equilibrium models
by Laforte, Jean-Philippe, Ph.D.  Princeton University. 2008: 185 pages; 3295305.
24.
Integrating Exponential Dispersion Models to Latent Structures
by Basbug, Mehmet Emin, Ph.D.  Princeton University. 2017: 125 pages; 10254057.
25.
Statistical methods for research in healthcare quality and safety from observational data
by Myers, Jessica, Ph.D.  The Johns Hopkins University. 2010: 113 pages; 3428625.
27.
The Performance of Random Prototypes in Hierarchical Models of Vision
by Stewart, Kendall Lee, M.S.  Portland State University. 2015: 79 pages; 1605894.
28.
Representing Linguistic Knowledge with Probabilistic Models
by Meylan, Stephan Charles, Ph.D.  University of California, Berkeley. 2018: 206 pages; 10931065.
29.
Early prediction of student goals and affect in narrative-centered learning environments
by Lee, Sunyoung, Ph.D.  North Carolina State University. 2008: 98 pages; 3329207.
30.
Bayesian lasso: An extension for genome-wide association study
by Joo, LiJin, Ph.D.  New York University. 2017: 119 pages; 10243856.
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