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1.
Computational Bayesian Methods Applied to Complex Problems in Bio and Astro Statistics
by Elrod, Chris, Ph.D.  Baylor University. 2019: 103 pages; 27540692.
4.
Distribution Matching Methods for Parameter Estimation and Model Selection in Computational Biology
by Lillacci, Gabriele, Ph.D.  University of California, Santa Barbara. 2012: 252 pages; 3545071.
5.
Bayesian Nonparametric Methods for Analysis of Electronic Health Records
by Xu, Dandan, Ph.D.  University of Florida. 2016: 108 pages; 10679207.
7.
Methods in Computational Cosmology
by Vakili, Mohammadjavad, Ph.D.  New York University. 2017: 235 pages; 10260795.
8.
Computational inference of genetic regulatory networks in human cancer cells
by Margolin, Adam Arne, Ph.D.  Columbia University. 2008: 282 pages; 3299359.
9.
Radiation Source Mapping with Bayesian Inverse Methods
by Hykes, Joshua Michael, Ph.D.  North Carolina State University. 2012: 198 pages; 3538540.
10.
Bayesian Biclustering on Discrete Data: Variable Selection Methods
by Guo, Lei, Ph.D.  Harvard University. 2013: 143 pages; 3600176.
11.
Objective Bayesian inference for stress-strength models and Bayesian ANOVA
by Min, Xiaoyi, Ph.D.  University of Missouri - Columbia. 2012: 128 pages; 3537873.
12.
Clustering algorithms for next-generation sequencing data from heterogenous populations
by Prabhakara, Shruthi, Ph.D.  The Pennsylvania State University. 2012: 126 pages; 3737327.
13.
Statistical Methods for High Dimensional Variable Selection
by Li, Kaiqiao, Ph.D.  State University of New York at Stony Brook. 2019: 114 pages; 13427291.
14.
Computational approaches to study the relation between genomic variations and phenotypes
by Huang, Hailiang, Ph.D.  The Johns Hopkins University. 2012: 212 pages; 3528554.
15.
Bayesian methods for estimating ROC curves
by Zur, Richard Michael, Ph.D.  The University of Chicago. 2010: 194 pages; 3432839.
16.
Applications of Genomic Resources and Bayesian Inference Methods to the Breeding of Peach [Prunus persica (L.) Batsch]
by Fresnedo Ramirez, Jonathan, Ph.D.  University of California, Davis. 2014: 168 pages; 3646288.
17.
Efficient Computational Methods for the Study of Charge Transport in Disordered Organic Semiconductors
by Brown, J. S., Ph.D.  University of Colorado at Boulder. 2019: 153 pages; 27663249.
18.
A Bayesian Approach to Reduced Order Modeling in Catalytic Steam Reforming
by Ford, Evan D., M.S.  West Virginia University. 2015: 61 pages; 1596597.
19.
Computational Mechanisms of Selective Attention During Reinforcement Learning
by Radulescu, Angela , Ph.D.  Princeton University. 2020: 133 pages; 27964412.
20.
Quantifying Protonation State Effects in Protein-ligand Interaction
by Rustenburg, AriĆ«n Sebastiaan, Ph.D.  Weill Medical College of Cornell University. 2019: 308 pages; 27543710.
21.
Computational Methods for Modeling Enzymes
by Bertolani, Steve James, Ph.D.  University of California, Davis. 2018: 310 pages; 10928544.
22.
Bayesian lasso: An extension for genome-wide association study
by Joo, LiJin, Ph.D.  New York University. 2017: 119 pages; 10243856.
23.
Characterization of protein function using automated computational methods
by Wu, Shirley, Ph.D.  Stanford University. 2009: 179 pages; 3364515.
24.
Bayesian mortgage default models
by Xu, Feng, Ph.D.  The George Washington University. 2008: 157 pages; 3316109.
25.
Bayesian generative modeling for complex dynamical systems
by Guan, Jinyan, Ph.D.  The University of Arizona. 2016: 120 pages; 10109036.
26.
Uncertainty Aggregation in System Performance Assessment
by Nannapanemi, Saideep, Ph.D.  Vanderbilt University. 2017: 188 pages; 13835161.
27.
Essays in the Dynamics Bayesian Models in Marketing
by Kim, Bumsoo, Ph.D.  The George Washington University. 2013: 100 pages; 3591693.
28.
Inferring Optimally Precise and Maximally Accurate Models from Electron Microscopy Data
by Greenberg, Charles Harold, Ph.D.  University of California, San Francisco. 2016: 125 pages; 10165386.
29.
Computational Imaging Methods for Improving Resolution in Biological Microscopy
by Chan, Kevin G., Ph.D.  University of California, Santa Barbara. 2017: 104 pages; 10263523.
30.
Decoding the Computations of Sensory Neurons
by Kaardal, Joel Thomas, Ph.D.  University of California, San Diego. 2017: 143 pages; 10633582.
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