ICASSP 2016accepted0 citations
Parallel metropolis chains with cooperative adaptation
Luca Martino, Victor Elvira, David Luengo, Francisco Louzada
Abstract
Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have become very popular in signal processing over the last years. In this work, we introduce a novel MCMC scheme where parallel MCMC chains interact, adapting cooperatively the parameters of their proposal functions. Furthermore, the novel algorithm distributes the computational effort adaptively, rewarding the chains which are providing better performance and, possibly even stopping other ones. These extinct chains can be reactivated if the algorithm considers it necessary. Numerical simulations show the benefits of the novel scheme.
BibTeX
@inproceedings{icassp2016_parallelmetropol,
title = {Parallel metropolis chains with cooperative adaptation},
author = {Luca Martino and Victor Elvira and David Luengo and Francisco Louzada},
booktitle = {ICASSP 2016},
year = {2016}
}