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Article overview
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Particle Smoothing for Hidden Diffusion Processes: Adaptive Path Integral Smoother | H.-Ch. Ruiz
; H. J. Kappen
; | Date: |
1 May 2016 | Abstract: | Particle smoothing methods are used for inference of stochastic processes
based on noisy observations. Typically, the estimation of the marginal
posterior distribution given all observations is cumbersome and computational
intensive. In this paper, we propose a simple algorithm based on path integral
control theory to estimate the smoothing distribution of continuous-time
diffusion processes with partial observations. In particular, we use an
adaptive importance sampling method to improve the effective sampling size of
the posterior over processes given the observations and the reliability of the
estimation of the marginals. This is achieved by estimating a feedback
controller to sample efficiently from the joint smoothing distributions. We
compare the results with estimations obtained from the standard Forward
Filter/Backward Simulator for two diffusion processes of different complexity.
We show that the proposed method gives more reliable estimations than the
standard FFBSi when the smoothing distribution is poorly represented by the
filter distribution. | Source: | arXiv, 1605.0278 | Services: | Forum | Review | PDF | Favorites |
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