2014
Martino, Luca; Elvira, Víctor; Luengo, David; Artés-Rodríguez, Antonio; Corander, Jukka
Orthogonal MCMC Algorithms Proceedings Article
En: 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo
@inproceedings{Martino2014b,
title = {Orthogonal MCMC Algorithms},
author = {Luca Martino and V\'{i}ctor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},
url = {http://edas.info/p15153#S1569490857},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},
address = {Gold Coast},
abstract = {Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown 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 using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.},
keywords = {Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo},
pubstate = {published},
tppubtype = {inproceedings}
}
Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown 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 using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.