Journal of Signal Processing Systems

, Volume 61, Issue 1, pp 51–59

A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems

  • Yuan Shen
  • Cedric Archambeau
  • Dan Cornford
  • Manfred Opper
  • John Shawe-Taylor
  • Remi Barillec
Article

DOI: 10.1007/s11265-008-0299-y

Cite this article as:
Shen, Y., Archambeau, C., Cornford, D. et al. J Sign Process Syst (2010) 61: 51. doi:10.1007/s11265-008-0299-y
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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.

Keywords

Data assimilationSignal processingNonlinear smoothingVariational approximationBayesian computation

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Yuan Shen
    • 1
  • Cedric Archambeau
    • 2
  • Dan Cornford
    • 1
  • Manfred Opper
    • 3
  • John Shawe-Taylor
    • 2
  • Remi Barillec
    • 1
  1. 1.Neural Computing Research GroupAston UniversityBirminghamUK
  2. 2.Department of Computer ScienceUniversity College LondonLondonUK
  3. 3.Artificial Intelligence GroupTechnical University BerlinBerlinGermany