A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems

作者：Shen Yuan^*; Archambeau Cedric; Cornford Dan; Opper Manfred; Shawe Taylor John; Barillec Remi

来源：Journal of Signal Processing Systems for Signal Image and Video Technology, 2010, 61(1): 51-59.

DOI：10.1007/s11265-008-0299-y

• Data assimilation
• Signal processing
• Nonlinear smoothing
• Variational approximation
• Bayesian computation

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.

• 出版日期2010-10