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

Publication Type: 
Refereed Original Article
Abstract: 
Gibbs random fi elds play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoods o er 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.
Digital Object Identifer (DOI): 
10.NA
Publication Status: 
Published
Publication Date: 
11/05/2015
Journal: 
JMLR Workshop and Conference Proceedings
Volume: 
38
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
Yes
Publication document: