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Bayesian computation for statistical models with intractable normalizing constants

Abstract : This paper deals with some computational aspects in 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 an approach to sample from such posterior distributions. The method can be thought as a Bayesian version of the MCMC-MLE approach of Geyer and Thompson (1992). To the best of our knowledge, this is the first general and asymptotically consistent Monte Carlo method for such problems. We illustrate the method with 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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https://hal.archives-ouvertes.fr/hal-00274615
Contributor : Christian Robert Connect in order to contact the contributor
Submitted on : Saturday, April 19, 2008 - 5:46:27 PM
Last modification on : Monday, October 11, 2021 - 1:24:06 PM

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  • HAL Id : hal-00274615, version 1
  • ARXIV : 0804.3152

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Yves Atchade, Nicolas Lartillot, Christian Robert. Bayesian computation for statistical models with intractable normalizing constants. 2008. ⟨hal-00274615⟩

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