Browsing Theses and Dissertations by Title
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Bayesian and InformationTheoretic Learning of High Dimensional Data
(2012)The concept of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated ... 
Bayesian Approaches to File Linking with Faulty Data
(2017)File linking allows analysts to combine information from two or more sources of information, creating linked data bases. From linking school records to track student progress across years, to official statistics and linking ... 
Bayesian Computation for HighDimensional Continuous & Sparse Count Data
(2018)Probabilistic modeling of multidimensional data is a common problem in practice. When the data is continuous, one common approach is to suppose that the observed data are close to a lowerdimensional smooth manifold. There ... 
Bayesian Density Regression With a Jump Discontinuity at a Given Threshold
(2019)Standard regression discontinuity design usually concentrates on the causal effects by assigning a threshold above or below which an intervention is assigned. By com paring the real values of observations near the threshold, ... 
Bayesian Dynamic Modeling and Forecasting of Count Time Series
(2019)Problems of forecasting related time series of counts arise in a diverse array of applications such as consumer sales, epidemiology, ecology, law enforcement, and tourism. Characteristics of highfrequency count data including ... 
Bayesian Dynamic Modeling for Streaming Network Data
(2017)Streaming network data of various forms arises in many applications, raising interest in research to model and quantify the nature of stochasticity and structure in dynamics underlying such data. One example context is that ... 
Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis
(2016)The advances in three related areas of statespace modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme ... 
Bayesian Estimation and Sensitivity Analysis for Causal Inference
(2019)This disseration aims to explore Bayesian methods for causal inference. In chapter 1, we present an overview of fundamental ideas from causal inference along with an outline of the methodological developments that we hope ... 
Bayesian Hierarchical Models for Model Choice
(2013)With the development of modern data collection approaches, researchers may collect hundreds to millions of variables, yet may not need to utilize all explanatory variables available in predictive models. Hence, choosing ... 
Bayesian Hierarchical Models to Address Problems in Neuroscience and Economics
(2017)In the first chapter, motivated by a model used to analyze spike train data, we present a method for learning multiple probability vectors by using information from large samples to improve estimates for smaller samples. ... 
Bayesian Inference in Largescale Problems
(2016)Many modern applications fall into the category of "largescale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point ... 
Bayesian Inference Via Partitioning Under Differential Privacy
(2018)In this thesis, I develop differentially private methods to report posterior probabilities and posterior quantiles of linear regression coefficients. I accomplish this by randomly partitioning the data, taking an intermediate ... 
Bayesian Kernel Models for Statistical Genetics and Cancer Genomics
(2017)The main contribution of this thesis is to examine the utility of kernel regression ap proaches and variance component models for solving complex problems in statistical genetics and molecular biology. Many of these types ... 
Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas
(2016)Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden ... 
Bayesian metaanalysis models for heterogeneous genomics data
(2013)The accumulation of highthroughput data from vast sources has drawn a lot attentions to develop methods for extracting meaningful information out of the massive data. More interesting questions arise from how to combine ... 
Bayesian Methods for TwoSample Comparison
(2015)Twosample comparison is a fundamental problem in statistics. Given two samples of data, the interest lies in understanding whether the two samples were generated by the same distribution or not. Traditional twosample comparison ... 
Bayesian Methods to Characterize Uncertainty in Predictive Modeling of the Effect of Urbanization on Aquatic Ecosystems
(2010)Urbanization causes myriad changes in watershed processes, ultimately disrupting the structure and function of stream ecosystems. Urban development introduces contaminants (human waste, pesticides, industrial chemicals). ... 
Bayesian Mixture Modeling Approaches for Intermediate Variables and Causal Inference
(2010)This thesis examines causal inference related topics involving intermediate variables, and uses Bayesian methodologies to advance analysis capabilities in these areas. First, joint modeling of outcome variables with intermediate ... 
Bayesian Model Uncertainty and Foundations
(2018)This dissertation contains research on Bayesian model uncertainty and foundations of statistical inference. In Chapter 2, we study the properties of constrained empirical Bayes (EB) priors on regression coefficients. Unrestricted ... 
Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
(2010)The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prior ...