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

doi:10.1007/s11265-008-0299-y

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

Computer Science Research Group

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	51-59
Number of pages	9
Journal	Journal of Signal Processing Systems
Volume	61
Issue number	1
Early online date	11 Nov 2008
DOIs	https://doi.org/10.1007/s11265-008-0299-y
Publication status	Published - Oct 2010
Event	IEEE International Workshop on Machine Learning for Signal Processing - Thessaloniki, Greece Duration: 27 Aug 2007 → 29 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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10.1007/s11265-008-0299-y

Comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems
The original publication is available at www.springerlink.com

http://www.springerlink.com/content/qt57174m00231v04/

Cite this

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

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. {\textcopyright} 2008 Springer Science + Business Media LLC.",

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author = "Yuan Shen and C{\'e}dric Archambeau and Dan Cornford and Manfred Opper and John Shawe-Taylor and Remi Barillec",

note = "The original publication is available at www.springerlink.com; IEEE International Workshop on Machine Learning for Signal Processing ; Conference date: 27-08-2007 Through 29-08-2007",

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language = "English",

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T1 - A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

AU - Shen, Yuan

AU - Archambeau, Cédric

AU - Cornford, Dan

AU - Opper, Manfred

AU - Shawe-Taylor, John

AU - Barillec, Remi

N1 - The original publication is available at www.springerlink.com

PY - 2010/10

Y1 - 2010/10

N2 - 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.

AB - 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.

KW - Bayesian computation

KW - data assimilation

KW - nonlinear smoothing

KW - signal processing

KW - variational approximation

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M3 - Article

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EP - 59

JO - Journal of Signal Processing Systems

JF - Journal of Signal Processing Systems

IS - 1

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 27 August 2007 through 29 August 2007

ER -

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

Abstract

Bibliographical note

Keywords

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