While the classical framework has a rich set of limited information procedures such as GMM and other related methods, the situation is not so in the Bayesian framework. We develop a limited information procedure in the Bayesian framework that does not require the knowledge of the full likelihood. The developed procedure is a Bayesian counterpart of the classical GMM but has advantages over the ...

Bayesian partially identified models have received a growing attention in recent years in the econometric literature, due to their broad applications in empirical studies. Classical Bayesian approach in this literature has been assuming a parametric model, by specifying an ad-hoc parametric likelihood function. However, econometric models usually only identify a set of moment inequalities, and ...

There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM’s strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can exte...

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 develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutation-based techniques for sampling under the null distribution. In some situations, however, a full parametric model cannot be justified, or the sample siz...

We develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutation-based techniques for sampling under the null distribution. In some situations, however, a full parametric model cannot be justified, or the sample siz...

Most existing semi-parametric estimation procedures for binary choice models are based on the maximum score, maximum likelihood, or nonlinear least squares principles. These methods have two problems. They are difficult to compute and they may result in multiple local optima because they require optimizing nonlinear objective functions which may not be unimode. These problems are exacerbated wh...

A semi-parametric regression methodology is formulated to identify the unsteady lift characteristics of a small UAS undergoing dynamic stall. Based on trailing edge separation model Leishmann and Beddoes, nonlinear evolution point so that it can be estimated by non-parametric Machine Learning methods. Validation presented with identification coefficient based quasi-steady wind tunnel tests.