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2016 (engelsk)Inngår i: Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016, Vol. 48, s. 2616-2625Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both non-interacting PMCMC samplers and a single PMCMC sampler with an equivalent memory and computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.
Serie
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 48
Emneord
Sequential Monte Carlo, Probabilistic programming, parallelisation
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-130043 (URN)
Konferanse
International Conference on Machine Learning (ICML), New York, USA, June 19-24, 2016
Prosjekter
CADICS
Forskningsfinansiär
Cancer and Allergy Foundation
2016-07-052016-07-052022-03-01bibliografisk kontrollert