Bouranis, LamprosLamprosBouranisFriel, NialNialFrielMaire, FlorianFlorianMaire2019-05-132019-05-132018 Elsev2018-07-23Computational Statistics & Data Analysishttp://hdl.handle.net/10197/10402The reversible jump Markov chain Monte Carlo (RJMCMC) method offers an across-model simulation approach for Bayesian estimation and model comparison, by exploring the sampling space that consists of several models of possibly varying dimensions. A naive implementation of RJMCMC to models like Gibbs random fields suffers from computational difficulties: the posterior distribution for each model is termed doubly-intractable since computation of the likelihood function is rarely available. Consequently, it is simply impossible to simulate a transition of the Markov chain in the presence of likelihood intractability. A variant of RJMCMC is presented, called noisy RJMCMC, where the underlying transition kernel is replaced with an approximation based on unbiased estimators. Based on previous theoretical developments, convergence guarantees for the noisy RJMCMC algorithm are provided. The experiments show that the noisy RJMCMC algorithm can be much more efficient than other exact methods, provided that an estimator with controlled Monte Carlo variance is used, a fact which is in agreement with the theoretical analysis.enThis is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Data Analysis (128, (2018)) https://doi.org/10.1016/j.csda.2018.07.005Bayes factorsIntractable likelihoodsMarkov random fieldsNoisy MCMCModel comparison for Gibbs random fields using noisy reversible jump Markov chain Monte CarloJournal Article12822124110.1016/j.csda.2018.07.0052018-09-26SFI/12/RC/228912/IP/1424https://creativecommons.org/licenses/by-nc-nd/3.0/ie/