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Nested Sequential Monte Carlo Methods
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
The University of Cambridge, Cambridge, United Kingdom.
Uppsala University, Uppsala, Sweden.
2015 (English)In: Proceedings of The 32nd International Conference on Machine Learning / [ed] Francis Bach, David Blei, Journal of Machine Learning Research (Online) , 2015, Vol. 37, 1292-1301 p.Conference paper (Refereed)
Abstract [en]

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.

Place, publisher, year, edition, pages
Journal of Machine Learning Research (Online) , 2015. Vol. 37, 1292-1301 p.
, JMLR Workshop and Conference Proceedings, ISSN 1938-7228 ; 37
National Category
Computer Science Control Engineering Probability Theory and Statistics
URN: urn:nbn:se:liu:diva-122698OAI: diva2:871698
32nd International Conference on Machine Learning, Lille, France, 6-11 July, 2015
Available from: 2015-11-16 Created: 2015-11-16 Last updated: 2015-12-02Bibliographically approved

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