Variational Bayesian inference for partially observed stochastic dynamical systems

Wang, B. and Titterington, D.M. (2009) Variational Bayesian inference for partially observed stochastic dynamical systems. Journal of Physics: Conference Series, 143(1), 012-022. (doi:10.1088/1742-6596/143/1/012022)

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Publisher's URL: http://dx.doi.org/10.1088/1742-6596/143/1/012022

Abstract

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Titterington, Professor Michael
Authors: Wang, B., and Titterington, D.M.
Subjects:Q Science > QA Mathematics
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of Physics: Conference Series
ISSN:1742-6596

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