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Local Sequential Monte Carlo Methods

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

Sequential Monte Carlo methods (often termed particle filters in this context) are one of the most versatile computational approaches to the (discrete time) filtering problem. The work presented develops techniques which allow almost automatic block-sampling in this setting. This approach substantially improving the path-space performance of these algorithms, allowing online inference in settings in which the whole trajectory of the unobserved Markov process is of interest. Results for simple examples illustrate the potential of the proposed approach.

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

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