Bayesian computation for statistical models with intractable normalizing constants

Details Bayesian computation for statistical models with intractable normalizing constants Bayesian computation for statistical models with intractable normalizing constants

2008-04-21

arxiv.org/abs/0804.3152v1

Statistics

Computation

20 pages, 4 figures, submitted for publication

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Bayesian computation for statistical models with intractable normalizing constants does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Bayesian computation for statistical models with intractable normalizing constants, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Bayesian computation for statistical models with intractable normalizing constants will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-35922