论文标题
贝叶斯的两步大都会黑斯廷斯方法用于贝叶斯模型选择的应用
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection
论文作者
论文摘要
最近，经验可能性已在贝叶斯框架下广泛应用。马尔可夫链蒙特卡洛（MCMC）方法经常用于从感兴趣参数的后验分布中采样。然而，在选择适当的MCMC算法时，复杂的可能性支持的复杂性，尤其是非凸性的性质，建立了巨大的障碍。在许多应用中，这种困难限制了基于贝叶斯的经验可能性（雪茄）方法的使用。在本文中，我们提出了一个两步的大都会黑斯廷斯算法，以从贝耶斯后期进行采样。我们的建议是在层次上指定的，其中确定经验可能性的估计方程用于提出一组参数的值，具体取决于其余参数的提议值。此外，我们使用经验可能性讨论贝叶斯模型的选择，并将我们的两步大都会黑斯廷斯算法扩展到可逆的跳跃马尔可夫链蒙特卡洛手术程序，从结果后部进行采样。最后，提出了我们提出的方法的几种应用。
In recent times empirical likelihood has been widely applied under Bayesian framework. Markov chain Monte Carlo (MCMC) methods are frequently employed to sample from the posterior distribution of the parameters of interest. However, complex, especially non-convex nature of the likelihood support erects enormous hindrances in choosing an appropriate MCMC algorithm. Such difficulties have restricted the use of Bayesian empirical likelihood (BayesEL) based methods in many applications. In this article, we propose a two-step Metropolis Hastings algorithm to sample from the BayesEL posteriors. Our proposal is specified hierarchically, where the estimating equations determining the empirical likelihood are used to propose values of a set of parameters depending on the proposed values of the remaining parameters. Furthermore, we discuss Bayesian model selection using empirical likelihood and extend our two-step Metropolis Hastings algorithm to a reversible jump Markov chain Monte Carlo procedure to sample from the resulting posterior. Finally, several applications of our proposed methods are presented.
