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)

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 D
• 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