Bayesian inference for Gibbs random fields using composite likelihoods
Computation
2012-07-25 v1
Abstract
Gibbs random fields play an important role in statistics, for example the autologistic model is commonly used to model the spatial distribution of binary variables defined on a lattice. However they are complicated to work with due to an intractability of the likelihood function. It is therefore natural to consider tractable approximations to the likelihood function. Composite likelihoods offer a principled approach to constructing such approximation. The contribution of this paper is to examine the performance of a collection of composite likelihood approximations in the context of Bayesian inference.
Cite
@article{arxiv.1207.5758,
title = {Bayesian inference for Gibbs random fields using composite likelihoods},
author = {Nial Friel},
journal= {arXiv preprint arXiv:1207.5758},
year = {2012}
}
Comments
To appear in the proceedings of the 2012 Winter Simulation Conference