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Rao-Blackwellized Particle Smoothing for Conditionally Linear Gaussian Models (NOTICE CHANGED TIME!)

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If you have a question about this talk, please contact Rachel Fogg .

Although Monte Carlo based particle filters and smoothers can be used for approximate inference in almost any kind of probabilistic state space models, the required number of samples for a sufficient accuracy can be high. The efficiency of sampling can be improved by Rao-Blackwellization, where part of the state is marginalized out in closed form, and only the remaining part is sampled. Because the sampled space has a lower dimension, fewer particles are required. In this talk I will discuss on Rao-Blackwellization in the context of conditionally linear Gaussian models, and present efficient Rao-Blackwellized versions of previously proposed particle smoothers.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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LCHES Seminar Room, Henry Wellcome Building Fitzwilliam Street Webinar & FW11, Computer Laboratory, William Gates Building. CMS, Pav. B, MR9 (B0.09)

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