Options
Efficient MCMC for Gibbs Random Fields using pre-computation
Author(s)
Date Issued
2018-05-31
Date Available
2019-07-09T11:19:29Z
Abstract
Bayesian inference of Gibbs random fields (GRFs) is often referred to as a doubly intractable problem, since the likelihood function is intractable. The exploration of the posterior distribution of such models is typically carried out with a sophisticated Markov chain Monte Carlo (MCMC) method, the exchange algorithm (Murray et al., 2006), which requires simulations from the likelihood function at each iteration. The purpose of this paper is to consider an approach to dramatically reduce this computational overhead. To this end we introduce a novel class of algorithms which use realizations of the GRF model, simulated offline, at locations specified by a grid that spans the parameter space. This strategy speeds up dramatically the posterior inference, as illustrated on several examples. However, using the pre-computed graphs introduces a noise in the MCMC algorithm, which is no longer exact. We study the theoretical behaviour of the resulting approximate MCMC algorithm and derive convergence bounds using a recent theoretical development on approximate MCMC methods.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Journal Article
Publisher
The Institute of Mathematical Statistics and the Bernoulli Society
Journal
 Electronic Journal of Statistics
Volume
12
Issue
2
Start Page
4138
End Page
4179
Copyright (Published Version)
2018 the Authors
Language
English
Status of Item
Peer reviewed
ISSN
1935-7524
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
5
Acquisition Date
Apr 16, 2024
Apr 16, 2024
Views
651
Acquisition Date
Apr 16, 2024
Apr 16, 2024
Downloads
247
Last Week
3
3
Acquisition Date
Apr 16, 2024
Apr 16, 2024