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


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.

Publication DOI: https://doi.org/10.1007/s11265-008-0299-y
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: Bayesian computation,data assimilation,nonlinear smoothing,signal processing,variational approximation,Hardware and Architecture,Information Systems,Signal Processing,Theoretical Computer Science,Control and Systems Engineering,Modelling and Simulation
Publication ISSN: 1939-8115
Full Text Link: http://www.spri ... 57174m00231v04/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2010-10
Published Online Date: 2008-11-11
Authors: Shen, Yuan
Archambeau, Cédric
Cornford, Dan (ORCID Profile 0000-0001-8787-6758)
Opper, Manfred
Shawe-Taylor, John
Barillec, Remi

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