A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Yuan Shen*, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Barillec

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.

Original languageEnglish
Pages (from-to)51-59
Number of pages9
JournalJournal of Signal Processing Systems
Volume61
Issue number1
Early online date11 Nov 2008
DOIs
Publication statusPublished - Oct 2010
EventIEEE International Workshop on Machine Learning for Signal Processing - Thessaloniki, Greece
Duration: 27 Aug 200729 Aug 2007

Bibliographical note

The original publication is available at www.springerlink.com

Keywords

  • Bayesian computation
  • data assimilation
  • nonlinear smoothing
  • signal processing
  • variational approximation

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