Evaluation 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 and Remi Barillec
In: MLSP 17, 27-29 Aug 2007, Thessaloniki, Greece.

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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.

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Available Versions of this Item

• Gaussian Process Approximations of Stochastic Differential Equations (deposited 22 November 2006)
  • Gaussian Process Approximations of Stochastic Differential Equations (deposited 21 December 2007)
  • Variational Inference for Diffusion Processes (deposited 21 December 2007)
    • Stochastic Memoizer for Sequence Data (deposited 08 March 2010)
  • Evaluation of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems (deposited 21 December 2007) [Currently Displayed]
    • Evaluation of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems (deposited 24 March 2009)

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