论文标题
参与MCMC：一个统一的框架
Involutive MCMC: a Unifying Framework
论文作者
论文摘要
马尔可夫链蒙特卡洛（MCMC）是一种用于推理，集成，优化和仿真等基本问题的计算方法。该领域已经开发了广泛的算法，它们的动机方式，应用方式以及采样的有效性。尽管存在所有差异，但其中许多具有相同的核心原则，我们将其统一为参与的MCMC（IMCMC）框架。在此基础上，我们用IMCMC来描述各种MCMC算法，并制定了许多“技巧”，可以用作开发新的MCMC算法的设计原理。因此，IMCMC提供了许多已知的MCMC算法的统一视图，该算法促进了强大扩展的推导。我们用两个示例演示了后者，其中我们将已知的可逆MCMC算法转换为更有效的不可逆算法。
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation. The field has developed a broad spectrum of algorithms, varying in the way they are motivated, the way they are applied and how efficiently they sample. Despite all the differences, many of them share the same core principle, which we unify as the Involutive MCMC (iMCMC) framework. Building upon this, we describe a wide range of MCMC algorithms in terms of iMCMC, and formulate a number of "tricks" which one can use as design principles for developing new MCMC algorithms. Thus, iMCMC provides a unified view of many known MCMC algorithms, which facilitates the derivation of powerful extensions. We demonstrate the latter with two examples where we transform known reversible MCMC algorithms into more efficient irreversible ones.
