PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

There is a more recent version of this eprint available. Click here to view it.


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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3138
Deposited By:Cedric Archambeau
Deposited On:21 December 2007

Available Versions of this Item