OF THE DISSERTATION Mixture Block Methods for Non Parametric Bayesian Models with Applications By Ian Porteous Doctor of Philosophy in Computer Science University of California, Irvine, 2010 Professor Max Welling, Chair This study brings together Bayesian networks, topic models, hierarchical Bayes modeling and nonparametric Bayesian methods to build a framework for efficiently designing and imp...

We propose numerical and graphical methods for outlier detection in hierarchical Bayes modeling and analyses of repeated measures regression data from multiple subjects; data from a single subject are generically called a “curve.” The first-stage of our model has curve-specific regression coefficients with possibly autoregressive errors of a prespecified order. The first-stage regression vector...

OF THE DISSERTATION Networks of Mixture Blocks for Non Parametric Bayesian Models with Applications By Ian Porteous Doctor of Philosophy in Information and Computer Science University of California, Irvine, 2010 Professor Max Welling, Chair This study brings together Bayesian networks, topic models, hierarchical Bayes modeling and nonparametric Bayesian methods to build a framework for efficien...

Given a time series data, model dynamical systems are built using a hierarchical Bayesian scheme with feedforward neural nets and then the models are compared in terms of marginal likelihood. The model with the highest marginal likelihood is used for predictions. The algorithm is applied to building air-conditioning load prediction. INTRODUCTION When nonlinearity is present, time series predict...