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2.
Using Social Dynamics to Make Individual Predictions: Variational Inference with Stochastic Kinetic Model
by Xu, Zhen, Ph.D.  State University of New York at Buffalo. 2016: 118 pages; 10253123.
3.
Signatures of Approximate Bayesian Inference in Early Visual Perception
by Lange, Richard D., Ph.D.  University of Rochester. 2020: 360 pages; 28093511.
4.
Principles of Dynamics and Inference, with Applications to the Brain
by Weistuch, Corey, Ph.D.  State University of New York at Stony Brook. 2020: 89 pages; 28090345.
5.
Robust network inference with multivariate t-distributions
by Finegold, Michael A., Ph.D.  The University of Chicago. 2010: 94 pages; 3419771.
6.
7.
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.
8.
Methodology for Fleet Uncertainty Reduction with Unsupervised Learning
by Modarres, Ceena, M.S.  University of Maryland, College Park. 2016: 77 pages; 10253325.
9.
Inferring Interpretable Representations of Population Structure
by Marcus, Joseph Henry, Ph.D.  The University of Chicago. 2020: 201 pages; 28089505.
10.
Latent Variable Modeling for Networks and Text: Algorithms, Models and Evaluation Techniques
by Foulds, James Richard, Ph.D.  University of California, Irvine. 2014: 287 pages; 3631094.
11.
Efficient inference algorithms for near-deterministic systems
by Chatterjee, Shaunak, Ph.D.  University of California, Berkeley. 2013: 130 pages; 3616611.
12.
Bayesian lasso: An extension for genome-wide association study
by Joo, LiJin, Ph.D.  New York University. 2017: 119 pages; 10243856.
13.
Towards Effective and Controllable Neural Text Generation
by Fang, Le, Ph.D.  State University of New York at Buffalo. 2020: 88 pages; 27994869.
14.
Reinforcement Learning and Optimal Control in Complex Social Systems
by Yang, Fan, Ph.D.  State University of New York at Buffalo. 2020: 108 pages; 28087042.
15.
Unsupervised linguistic inference
by Lamar, Michael T., Ph.D.  Brown University. 2010: 165 pages; 3430192.
16.
Semi-Supervised Learning for Electronic Phenotyping in Support of Precision Medicine
by Halpern, Yonatan, Ph.D.  New York University. 2016: 353 pages; 10192124.
18.
Development of Multistep and Degenerate Variational Integrators for Applications in Plasma Physics
by Ellison, Charles Leland, Ph.D.  Princeton University. 2016: 272 pages; 10090246.
19.
Inference Algorithms and Sensorimotor Representations in Brains and Machines
by Mudigonda, Mayur, Ph.D.  University of California, Berkeley. 2019: 154 pages; 13904872.
20.
Nonparametric Bayes: Inference under nonignorable missingness and model selection
by Linero, Antonio, Ph.D.  University of Florida. 2015: 147 pages; 10299009.
21.
Integrating Exponential Dispersion Models to Latent Structures
by Basbug, Mehmet Emin, Ph.D.  Princeton University. 2017: 125 pages; 10254057.
22.
Beyond dynamic textures: A family of stochastic dynamical models for video with applications to computer vision
by Chan, Antoni Bert, Ph.D.  University of California, San Diego. 2008: 291 pages; 3331461.
23.
Latent Variable Modeling and Causal Inference in Population-Structured Genetics
by Cabreros, Irineo C., Ph.D.  Princeton University. 2020: 219 pages; 27546470.
24.
Performance analysis and inference of communication networks
by Ni, Jian, Ph.D.  Yale University. 2008: 178 pages; 3342752.
25.
Learning to Learn with Gradients
by Finn, Chelsea B., Ph.D.  University of California, Berkeley. 2018: 199 pages; 10930398.
26.
Conditional modeling and conditional inference
by Chang, Lo-Bin, Ph.D.  Brown University. 2010: 199 pages; 3430173.
27.
Parallel Junction Tree Algorithm on GPU
by Zheng, Lu, Ph.D.  Carnegie Mellon University. 2013: 94 pages; 3575068.
28.
Computational inference of genetic regulatory networks in human cancer cells
by Margolin, Adam Arne, Ph.D.  Columbia University. 2008: 282 pages; 3299359.
29.
Variational and Structural Methods in Mean Field Spin Glasses
by Jagannath, Aukosh Sundar, Ph.D.  New York University. 2016: 334 pages; 10139632.
30.
Static Randall-Sundrum Black Holes From a Variational Principle
by Fraser, Scott T., Ph.D.  University of California, Santa Barbara. 2010: 230 pages; 3427999.
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