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

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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.

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Original languageEnglish
Pages (from-to)51-59
Number of pages9
JournalJournal of Signal Processing Systems
Volume61
Issue1
Early online date11 Nov 2008
DOIs
StatePublished - Oct 2010
EventIEEE International Workshop on Machine Learning for Signal Processing - Thessaloniki, Greece

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The original publication is available at www.springerlink.com

    Keywords

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

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