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Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank WoodProceedings of The 33rd International Conference on Machine Learning, PMLR 48:2616-2625, 2016.
Abstract
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.
Cite this Paper
BibTeX
@InProceedings{pmlr-v48-rainforth16, title = {Interacting Particle Markov Chain Monte Carlo}, author = {Rainforth, Tom and Naesseth, Christian and Lindsten, Fredrik and Paige, Brooks and Vandemeent, Jan-Willem and Doucet, Arnaud and Wood, Frank}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {2616--2625}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/rainforth16.pdf}, url = {https://proceedings.mlr.press/v48/rainforth16.html}, abstract = {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.}} Endnote
%0 Conference Paper%T Interacting Particle Markov Chain Monte Carlo%A Tom Rainforth%A Christian Naesseth%A Fredrik Lindsten%A Brooks Paige%A Jan-Willem Vandemeent%A Arnaud Doucet%A Frank Wood%B Proceedings of The 33rd International Conference on Machine Learning%C Proceedings of Machine Learning Research%D 2016%E Maria Florina Balcan%E Kilian Q. Weinberger%F pmlr-v48-rainforth16%I PMLR%P 2616--2625%U https://proceedings.mlr.press/v48/rainforth16.html%V 48%X 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. RIS
TY - CPAPERTI - Interacting Particle Markov Chain Monte CarloAU - Tom RainforthAU - Christian NaessethAU - Fredrik LindstenAU - Brooks PaigeAU - Jan-Willem VandemeentAU - Arnaud DoucetAU - Frank WoodBT - Proceedings of The 33rd International Conference on Machine LearningDA - 2016/06/11ED - Maria Florina BalcanED - Kilian Q. WeinbergerID - pmlr-v48-rainforth16PB - PMLRDP - Proceedings of Machine Learning ResearchVL - 48SP - 2616EP - 2625L1 - http://proceedings.mlr.press/v48/rainforth16.pdfUR - https://proceedings.mlr.press/v48/rainforth16.htmlAB - 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.ER - APA
Rainforth, T., Naesseth, C., Lindsten, F., Paige, B., Vandemeent, J., Doucet, A. & Wood, F.. (2016). Interacting Particle Markov Chain Monte Carlo.Proceedings of The 33rd International Conference on Machine Learning, inProceedings of Machine Learning Research 48:2616-2625 Available from https://proceedings.mlr.press/v48/rainforth16.html.