Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Refereed Original Article
Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoods oer a principled means to construct useful approximations. This paper provides a mean to calibrate the posterior distribution result- ing from using a composite likelihood and il- lustrate its performance in several examples.
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JMLR Workshop and Conference Proceedings
National University of Ireland, Dublin (UCD)
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