Interacting Particle Markov Chain Monte Carlo
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
Text
Rainforth et al. "Interacting Particle Markov Chain Monte Carlo." International Conference on Machine Learning, 2016.Markdown
[Rainforth et al. "Interacting Particle Markov Chain Monte Carlo." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/rainforth2016icml-interacting/)BibTeX
@inproceedings{rainforth2016icml-interacting,
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 = {International Conference on Machine Learning},
year = {2016},
pages = {2616-2625},
volume = {48},
url = {https://mlanthology.org/icml/2016/rainforth2016icml-interacting/}
}