标题： 一种多臂老虎机MCMC，应用于从双重不可处理后验中抽样 显示英文标题

标题： A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior

摘要： 马尔可夫链蒙特卡罗（MCMC）算法被广泛用于从复杂分布中抽样，尤其是在贝叶斯推断中从后验分布中抽样。 然而，当面对双重不可处理问题时，MCMC无法直接应用。 在本文中，我们讨论并比较了两种现有的解决方案——伪边缘蒙特卡罗和交换算法。 本文还提出了一种新算法：多臂赌博机马尔可夫链蒙特卡罗（MABMC），该算法在每一步中在两个（或更多）随机接受比率之间进行选择。 MABMC可以直接结合伪边缘蒙特卡罗和交换算法，具有更高的平均接受概率。 显示英文摘要

摘要： Markov chain Monte Carlo (MCMC) algorithms are widely used to sample from complicated distributions, especially to sample from the posterior distribution in Bayesian inference. However, MCMC is not directly applicable when facing the doubly intractable problem. In this paper, we discussed and compared two existing solutions -- Pseudo-marginal Monte Carlo and Exchange Algorithm. This paper also proposes a novel algorithm: Multi-armed Bandit MCMC (MABMC), which chooses between two (or more) randomized acceptance ratios in each step. MABMC could be applied directly to incorporate Pseudo-marginal Monte Carlo and Exchange algorithm, with higher average acceptance probability.