Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Statistics Theory
2026-02-09 v2 Computation
Statistics Theory
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.
Cite
@article{arxiv.1502.01997,
title = {Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields},
author = {Julien Stoehr and Nial Friel},
journal= {arXiv preprint arXiv:1502.01997},
year = {2026}
}
Comments
JMLR Workshop and Conference Proceedings, 18th International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, California, USA, 9-12 May 2015 (Vol. 38, pp. 921-929). arXiv admin note: substantial text overlap with arXiv:1207.5758