PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes
Omiros Papaspiliopoulos, Chris Yau, Gareth Roberts and Chris Holmes
Journal of the Royal Statistical Society, series B 2008.

Abstract

We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. The method uses novel MCMC methodology which combines recent retrospective sampling methods with the use of slice sampler variables. The methodology is computationally efficient, both in terms of MCMC mixing properties, and robustness to the length of the time series being investigated. Moreover, the method is easy to implement requiring little or no user-interaction. We apply our methodology to the analysis of genomic copy number variation.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:5010
Deposited By:Omiros Papaspiliopoulos
Deposited On:24 March 2009