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This paper deals with some computational aspects in the Bayesian analysis ofstatistical models with intractable normalizing constants. In the presence ofintractable normalizing constants in the likelihood function, traditional MCMCmethods cannot be applied. We propose an approach to sample from such posteriordistributions. The method can be thought as a Bayesian version of the MCMC-MLEapproach of Geyer and Thompson (1992). To the best of our knowledge, this isthe first general and asymptotically consistent Monte Carlo method for suchproblems. We illustrate the method with examples from image segmentation andsocial network modeling. We study as well the asymptotic behavior of thealgorithm and obtain a strong law of large numbers for empirical averages.

Bayesian computation for statistical models with intractable normalizing constants