Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Y. Shen*, C. Archambeau, D. Cornford, M. Opper, J. Shawe-Taylor, R. Barillec

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 marginal variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother.

Original languageEnglish
Title of host publicationMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
PublisherIEEE
Pages306-311
Number of pages6
ISBN (Print)1424415667, 9781424415663
DOIs
Publication statusPublished - 1 Dec 2007
Event17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, United Kingdom
Duration: 27 Aug 200729 Aug 2007

Conference

Conference17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
CountryUnited Kingdom
CityThessaloniki
Period27/08/0729/08/07

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    Shen, Y., Archambeau, C., Cornford, D., Opper, M., Shawe-Taylor, J., & Barillec, R. (2007). Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems. In Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP (pp. 306-311). [4414324] IEEE. https://doi.org/10.1109/MLSP.2007.4414324