Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo

Files in This Item:
Access to this item has been restricted by the copyright holder until:2020-07-23
File Description SizeFormat 
insight_publication.pdf478.68 kBAdobe PDFDownload    Request a copy
Title: Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo
Authors: Bouranis, Lampros
Friel, Nial
Maire, Florian
Permanent link:
Date: 23-Jul-2018
Online since: 2019-05-13T09:08:31Z
Abstract: The reversible jump Markov chain Monte Carlo (RJMCMC) method offers an across-model simulation approach for Bayesian estimation and model comparison, by exploring the sampling space that consists of several models of possibly varying dimensions. A naive implementation of RJMCMC to models like Gibbs random fields suffers from computational difficulties: the posterior distribution for each model is termed doubly-intractable since computation of the likelihood function is rarely available. Consequently, it is simply impossible to simulate a transition of the Markov chain in the presence of likelihood intractability. A variant of RJMCMC is presented, called noisy RJMCMC, where the underlying transition kernel is replaced with an approximation based on unbiased estimators. Based on previous theoretical developments, convergence guarantees for the noisy RJMCMC algorithm are provided. The experiments show that the noisy RJMCMC algorithm can be much more efficient than other exact methods, provided that an estimator with controlled Monte Carlo variance is used, a fact which is in agreement with the theoretical analysis.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Computational Statistics & Data Analysis
Volume: 128
Start page: 221
End page: 241
Copyright (published version): 2018 Elsevier
Keywords: Bayes factorsIntractable likelihoodsMarkov random fieldsNoisy MCMC
DOI: 10.1016/j.csda.2018.07.005
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection

Show full item record

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.