Smelly parallel MCMC chains
Luca Martino, Víctor Elvira, David Luengo, Antonio Artés-Rodríguez, Jukka Corander
Abstract
Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.
BibTeX
@inproceedings{icassp2015_smellyparallelmc,
title = {Smelly parallel MCMC chains},
author = {Luca Martino and Víctor Elvira and David Luengo and Antonio Artés-Rodríguez and Jukka Corander},
booktitle = {ICASSP 2015},
year = {2015}
}