Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields

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Julien Stoehr, Nial Friel ;
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:921-929, 2015.

Abstract

Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoods offer a principled means to construct useful approximations. This paper provides a mean to calibrate the posterior distribution resulting from using a composite likelihood and illustrate its performance in several examples.

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