Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fieldsReport as inadecuate




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1 I3M - Institut de Mathématiques et de Modélisation de Montpellier 2 UCD - University College Dublin Dublin

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.

Keywords : Gibbs random fields Composite likelihoods Autologistic model





Author: Julien Stoehr - Nial Friel -

Source: https://hal.archives-ouvertes.fr/



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