Authors: Meïli Baragatti Agnès Grimaud Denys Pommeret
Publish Date: 2012/04/07
Volume: 23, Issue: 4, Pages: 535-549
Abstract
Approximate Bayesian Computational ABC methods or likelihoodfree methods have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable Several ABC methods have been proposed MCMC methods have been developed by Marjoram et al 2003 and by Bortot et al 2007 for instance and sequential methods have been proposed among others by Sisson et al 2007 Beaumont et al 2009 and Del Moral et al 2012 Recently sequential ABC methods have appeared as an alternative to ABCPMC methods see for instance McKinley et al 2009 Sisson et al 2007 In this paper a new algorithm combining populationbased MCMC methods with ABC requirements is proposed using an analogy with the parallel tempering algorithm Geyer 1991 Performance is compared with existing ABC algorithms on simulations and on a real example
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