Variational Bayesian Analysis for Hidden Markov Models

McGrory, C. A. and Titterington, D. M. (2009) Variational Bayesian Analysis for Hidden Markov Models. Australian and New Zealand Journal of Statistics, 51(2), pp. 227-244. (doi: 10.1111/j.1467-842X.2009.00543.x)

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The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach

Item Type:Articles
Keywords:Bayesian analysis deviance information criterion (DIC) DISTRIBUTIONS hidden Markov model INFERENCE MODEL MODELS SELECTION variational approximation
Glasgow Author(s) Enlighten ID:Titterington, Professor D
Authors: McGrory, C. A., and Titterington, D. M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Australian and New Zealand Journal of Statistics

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