Browsing by Subject "Bayesian"
Now showing items 1-20 of 24
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A Bayesian Approach to Understanding Music Popularity
(2015-05-08)The Billboard Hot 100 has been the main record chart for popular music in the American music industry since its first official release in 1958. Today, this rank- ing is based upon the frequency of which a song is played ... -
A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems
(2017)In this paper, we develop an algorithm that utilizes a Bayesian strategy to determine a sequence of questions to play the 20 Question game. The algorithm is motivated with an application to active recommender systems. We ... -
Advances in Bayesian Hierarchical Modeling with Tree-based Methods
(2020)Developing flexible tools that apply to datasets with large size and complex structure while providing interpretable outputs is a major goal of modern statistical modeling. A family of models that are especially suitable ... -
Applications and Computation of Stateful Polya Trees
(2017)Polya trees are a class of nonparametric priors on distributions which are able to model absolutely continuous distributions directly, rather than modeling a discrete distribution over parameters of a mixing kernel to obtain ... -
Bayesian Computation for Variable Selection and Multivariate Forecasting in Dynamic Models
(2020)Challenges arise in time series analysis due to the need for sequential forecasting and updating of model parameters as data is observed. This dissertation presents techniques for efficient Bayesian computation in multivariate ... -
Bayesian Inference in Large-scale Problems
(2016)Many modern applications fall into the category of "large-scale" 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 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 Multivariate Count Models for the Analysis of Microbiome Studies
(2019)Advances in high-throughput DNA sequencing allow for rapid and affordable surveys of thousands of bacterial taxa across thousands of samples. The exploding availability of sequencing data has poised microbiota research to ... -
Bayesian Semi-parametric Factor Models
(2012)Identifying a lower-dimensional latent space for representation of high-dimensional observations is of significant importance in numerous biomedical and machine learning applications. In many such applications, it is now ... -
Bayesian Structural Phylogenetics
(2013)This thesis concerns the use of protein structure to improve phylogenetic inference. There has been growing interest in phylogenetics as the number of available DNA and protein sequences continues to grow rapidly and demand ... -
Bayesian Techniques for Adaptive Acoustic Surveillance
(2010)Automated acoustic sensing systems are required to detect, classify and localize acoustic signals in real-time. Despite the fact that humans are capable of performing acoustic sensing tasks with ease in a variety of situations, ... -
Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection
(2018)This thesis naturally divides itself into two sections. The first two chapters concernthe development of Bayesian semi-parametric models for arrival times. Chapter 2considers Bayesian inference for a Gaussian process modulated ... -
Data-driven Analysis of Heavy Quark Transport in Ultra-relativistic Heavy-ion Collisions
(2019)Heavy flavor observables provide valuable information on the properties of the hot and dense Quark-Gluon Plasma (QGP) created in ultra-relativistic heavy-ion collisions.Previous study has made significant progress regarding ... -
Dependent Hierarchical Bayesian Models for Joint Analysis of Social Networks and Associated Text
(2012)This thesis presents spatially and temporally dependent hierarchical Bayesian models for the analysis of social networks and associated textual data. Social network analysis has received significant recent attention and ... -
Development and Calibration of Reaction Models for Multilayered Nanocomposites
(2015)This dissertation focuses on the development and calibration of reaction models for multilayered nanocomposites. The nanocomposites comprise sputter deposited alternating layers of distinct metallic elements. Specifically, ... -
Ecosystem Response to a Changing Climate: Vulnerability, Impacts and Monitoring
(2017)Rising temperatures with increased drought pose three challenges for management of future biodiversity. First, are the species expected to be vulnerable concentrated in specific regions and habitats? Second, are the impacts ... -
Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales
(2021)Increasingly, drone-based photogrammetry has been used to measure size and body condition changes in marine megafauna. A broad range of platforms, sensors, and altimeters are being applied for these purposes, but there is ... -
MCMC Sampling Geospatial Partitions for Linear Models
(2021)Geospatial statistical approaches must frequently confront the problem of correctlypartitioning a group of geographical sub-units, such as counties, states, or precincts,into larger blocks which share information. Since ... -
Measuring Baseball Defensive Value Using Statcast Data
(2017)Multiple methods of measuring the defensive value of baseball players have been developed. These methods commonly rely on human batted ball charters, which inherently introduces the possibility of measurement error and lack ... -
Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
(2010)In this thesis, temporal and spatial dependence are considered within nonparametric priors to help infer patterns, clusters or segments in data. In traditional nonparametric mixture models, observations are usually assumed ...
