By the same authors

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

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

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Publication details

Title of host publicationINTERNATIONAL WORKSHOP ON STATISTICAL-MECHANICAL INFORMATICS 2008 (IW-SMI 2008)
DatePublished - 2009
Pages12022-12022
Number of pages11
PublisherIOP Publishing Ltd.
Place of PublicationBRISTOL
EditorsM Hayashi, JI Inoue, Y Kabashima, K Tanaka
Original languageEnglish
ISBN (Print)*****************

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.

    Research areas

  • LIKELIHOOD PARAMETER-ESTIMATION, EM ALGORITHM, CONTROL LAWS

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