In this paper, we introduce a reversible version of a genetically modified mode jumping Markov chain Monte Carlo algorithm (GMJMCMC) for inference on posterior model probabilities in complex model spaces, where the number of explanatory variables is prohibitively large for classical Markov Chain Monte Carlo methods. Unlike the earlier proposed GMJMCMC algorithm, the introduced algorithm is a proper MCMC and its limiting distribution corresponds to the posterior marginal model probabilities in the explored model space under reasonable regularity conditions.

翻译：在本文中,我们引入了一种可逆的转基因模式跳跃马尔科夫连锁蒙特卡洛算法(GMJMC ), 用于推断复杂模型空间的后方模型概率,因为对于古典马科夫连锁公司蒙特卡洛方法来说,解释变量的数量之大令人望而却步。 与先前提议的GMJMC算法不同,引入的算法是一种适当的MCMC,其有限的分布与在合理正常条件下探索模型空间的后端模型边际模型概率相对应。