Brazilian Journal of Probability and Statistics

Bayesian computation for statistical models with intractable normalizing constants

Yves F. Atchadé, Nicolas Lartillot, and Christian Robert

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This paper deals with a computational aspect of the Bayesian analysis of statistical models with intractable normalizing constants. In the presence of intractable normalizing constants in the likelihood function, traditional MCMC methods cannot be applied. We propose here a general approach to sample from such posterior distributions that bypasses the computation of the normalizing constant. Our method can be thought as a Bayesian version of the MCMC-MLE approach of Geyer and Thompson [J. Roy. Statist. Soc. Ser. B 54 (1992) 657–699]. We illustrate our approach on examples from image segmentation and social network modeling. We study as well the asymptotic behavior of the algorithm and obtain a strong law of large numbers for empirical averages.

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Braz. J. Probab. Stat., Volume 27, Number 4 (2013), 416-436.

First available in Project Euclid: 9 September 2013

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Monte Carlo methods adaptive Markov Chain Monte Carlo Bayesian inference doubly-intractable distributions Ising model


Atchadé, Yves F.; Lartillot, Nicolas; Robert, Christian. Bayesian computation for statistical models with intractable normalizing constants. Braz. J. Probab. Stat. 27 (2013), no. 4, 416--436. doi:10.1214/11-BJPS174.

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