Variational Bayesian inference for partially observed stochastic dynamical systems - art. no. 012022

B. Wang, D. M. Titterington

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationINTERNATIONAL WORKSHOP ON STATISTICAL-MECHANICAL INFORMATICS 2008 (IW-SMI 2008)
EditorsM Hayashi, JI Inoue, Y Kabashima, K Tanaka
Place of PublicationBRISTOL
PublisherIOP Publishing
Pages12022-12022
Number of pages11
ISBN (Print)*****************
Publication statusPublished - 2009
EventInternational Workshop on Statistical-Mechanical Informatics - Sendai
Duration: 14 Sept 200817 Sept 2008

Conference

ConferenceInternational Workshop on Statistical-Mechanical Informatics
CitySendai
Period14/09/0817/09/08

Keywords

  • LIKELIHOOD PARAMETER-ESTIMATION
  • EM ALGORITHM
  • CONTROL LAWS

Cite this