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Variational Bayesian analysis for hidden Markov models
Clare Anne McGrory and Mike Titterington
Australian and New Zealand Journal of Statistics
2006.
AbstractThe variational approach to Bayesian inference enables
simultaneous estimation of component parameters and model
complexity. An interesting feature of this approach is that it appears also to
lead 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 initialised with a large
number of hidden states, redundant states are eliminated as the
method converges to a solution, thereby leading to an automatic
selection of the number of hidden states. In addition, through the
use of a variational approximation, the Deviance Information
Criterion (DIC) for Bayesian model selection can be extended to the
hidden Markov model framework. Calculation of the DIC provides a
further tool for model selection which can be used in conjunction
with the variational approach. [Edit]
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