PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Bayesian inference for partially observed stochastic dynamical systems
Bo Wang and Mike Titterington
In: International Workshop on Statistical-Mechanical Informatics 2008, 14-17 Sep 2008, Sendai, Japan.


In this paper the variational Bayesian approximation for partially observed continuous time stochastic processes is studied. We derive an EM-like algorithm and describe its implementation. The variational Expectation step is explicitly solved using the method of conditional moment generating functions and stochastic partial differential equations. The numerical experiments demonstrate that the variational Bayesian estimate is more robust than the EM algorithm.

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EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4951
Deposited By:Mike Titterington
Deposited On:24 March 2009