Browsing by Subject "Bayesian statistics"
Now showing items 1-20 of 24
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Advancements in Probabilistic Machine Learning and Causal Inference for Personalized Medicine
(2019)In this dissertation, we present four novel contributions to the field of statistics with the shared goal of personalizing medicine to individual patients. These methods are developed to directly address problems in health ... -
Advances in Bayesian Modeling of Protein Structure Evolution
(2018)This thesis contributes to a statistical modeling framework for protein sequence and structure evolution. An existing Bayesian model for protein structure evolution is extended in two unique ways. Each of these model extensions ... -
Bayesian Adjustment for Multiplicity
(2009)This thesis is about Bayesian approaches for handling multiplicity. It considers three main kinds of multiple-testing scenarios: tests of exchangeable experimental units, tests for variable inclusion in linear regresson ... -
Bayesian Analysis and Computational Methods for Dynamic Modeling
(2009)Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient ... -
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 meta-analysis models for heterogeneous genomics data
(2013)The accumulation of high-throughput 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 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 Statistical Models of Cell-Cycle Progression at Single-Cell and Population Levels
(2014)Cell division is a biological process fundamental to all life. One aspect of the process that is still under investigation is whether or not cells in a lineage are correlated in their cell-cycle progression. Data on cell-cycle ... -
Clustering Multiple Related Datasets with a Hierarchical Dirichlet Process
(2011)I consider the problem of clustering multiple related groups of data. My approach entails mixture models in the context of hierarchical Dirichlet processes, focusing on their ability to perform inference on the unknown ... -
Computational Systems Biology of Saccharomyces cerevisiae Cell Growth and Division
(2014)Cell division and growth are complex processes fundamental to all living organisms. In the budding yeast, <italic>Saccharomyces cerevisiae</italic>, these two processes are known to be coordinated with one another as a cell's ... -
Dynamic modeling and Bayesian predictive synthesis
(2017)This dissertation discusses model and forecast comparison, calibration, and combination from a foundational perspective. For nearly five decades, the field of forecast combination has grown exponentially. Its practicality ... -
Employing Neural Language Models and A Bayesian Hierarchical Framework for Classification and Engagement Analysis of Misinformation on Social Media
(2022-04)While social media can be an effective tool for maintaining personal relationships and making global connections, it has become a powerful force in the damaging spread of misinformation, especially during universally difficult ... -
Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics
(2014)In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for the customer and freight ... -
General and Efficient Bayesian Computation through Hamiltonian Monte Carlo Extensions
(2017)Hamiltonian Monte Carlo (HMC) is a state-of-the-art sampling algorithm for Bayesian computation. Popular probabilistic programming languages Stan and PyMC rely on HMC’s generality and efficiency to provide automatic Bayesian ... -
Interfaces between Bayesian and Frequentist Multiplte Testing
(2015)This thesis investigates frequentist properties of Bayesian multiple testing procedures in a variety of scenarios and depicts the asymptotic behaviors of Bayesian methods. Both Bayesian and frequentist approaches to multiplicity ... -
Modeling Heterogeneity With Bayesian Additive Regression Trees
(2023)This work focuses on using Bayesian Additive Regression Trees (BART), a flexible and computationally efficient regression method, to model heterogeneity in data. In particular, we focus on the closely related tasks ... -
Monitoring and Improving Markov Chain Monte Carlo Convergence by Partitioning
(2015)Since Bayes' Theorem was first published in 1762, many have argued for the Bayesian paradigm on purely philosophical grounds. For much of this time, however, practical implementation of Bayesian methods was limited to a ... -
On Bayesian Analyses of Functional Regression, Correlated Functional Data and Non-homogeneous Computer Models
(2013)Current frontiers in complex stochastic modeling of high-dimensional processes include major emphases on so-called functional data: problems in which the data are snapshots of curves and surfaces representing fundamentally ... -
Probabilistic Models for Text in Social Networks
(2018)Text in social networks is a common form of data. Common examples include emails between coworkers, text messages in a group chat, or comments on Facebook. There is value in developing models for such data. Examples of related ... -
Probabilistic Models on Fibre Bundles
(2019)In this thesis, we propose probabilistic models on fibre bundles for learning the generative process of data. The main tool we use is the diffusion kernel and we use it in two ways. First, we build from the diffusion kernel ...
