Browsing by Subject "Nonparametric"
Now showing items 1-7 of 7
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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 Methods for Two-Sample Comparison
(2015)Two-sample 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 two-sample comparison ... -
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 ... -
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 ... -
Essays on the Econometrics of Option Prices
(2014)This dissertation develops new econometric techniques for use in estimating and conducting inference on parameters that can be identified from option prices. The techniques in question extend the existing literature in financial ... -
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 ...
