Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions


In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Uncontrolled Keywords: Markov chain Monte Carlo algorithms,non-linear diffusion processes,variational Gaussian process approximation,hybrid Monte Carlo
Last Modified: 26 Dec 2023 09:54
Date Deposited: 03 Mar 2010 11:07
PURE Output Type: Technical report
Published Date: 2010-01-19
Authors: Shen, Y.
Cornford, D. (ORCID Profile 0000-0001-8787-6758)
Opper, M.
Archambeau, C.


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