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

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

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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 marginal variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2007 IEEE.

Type: Proceedings paper
Title: Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems
DOI: 10.1109/MLSP.2007.4414324
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/79199
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