Nested Sequential Monte Carlo Methods
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)
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
Computer Science Control Engineering Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-122698OAI: oai:DiVA.org:liu-122698DiVA: diva2:871698
32nd International Conference on Machine Learning, Lille, France, 6-11 July, 2015