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arXiv · 1207.5758

Bayesian inference for Gibbs random fields using composite likelihoods

Nial Friel
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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.

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Nial Friel. 2012-07-24. Bayesian inference for Gibbs random fields using composite likelihoods. https://arxiv.org/abs/1207.5758

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