Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems.
Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP.
(pp. 306 - 311).
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.
|Title:||Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
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