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Abstract Details

Activity Number: 232
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #304838
Title: Nonparametric Bayesian Nonhomogeneous Hidden Markov Models
Author(s): Abhra Sarkar*+ and Anindya Bhadra and Bani K Mallick
Companies: Texas A&M University and Texas A&M University and Texas A&M University
Address: Department of Statistics, College Station, TX, 77843-3143, United States
Keywords: Nonhomogeneous hidden Markov models ; Bayesian nonparametric mixture models ; MCMC sampling ; Slice sampling ; Epidemic prediction

In this article a flexible Bayesian nonparametric model is proposed for nonhomogeneous hidden Markov models. The model is developed through the amalgamation of the ideas of hidden Markov models and predictor dependent stick-breaking processes. Computation is carried out using auxiliary variable representation of the model which enable us to perform exact MCMC sampling from the posterior. Furthermore, the model is extended to a situation when the predictors can simultaneously influence the transition dynamics of the hidden states as well as the emission distribution. Estimates of few steps ahead conditional predictive distributions of the response have been used as performance diagnostics for these models. The proposed methodology is illustrated through simulation experiments as well as analysis of a real data set concerned with the prediction of rainfall induced malaria epidemics.

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