Optimal Markov chain Monte Carlo sampling
Optimal Markov chain Monte Carlo sampling
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最优马尔可夫链蒙特卡罗采样
DOI：
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发表时间：
2013
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通讯作者：
Ting
中科院分区：
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作者：
Ting
本文是关于最优马尔可夫链蒙特卡罗(MCMC)抽样的综述文章。重点放在齐次马尔可夫链上。本文首先回顾了寻找最优转移矩阵的问题，该转移矩阵的定义是最小化MCMC估计量的渐近方差。本文随后回顾了局部最优采样器(LOS)，这是一种基于最优转移矩阵执行局部更新的MCMC采样。我们进行了一项模拟研究，将LOS与Metropolis-Hastings和Gibbs Ssamer进行了比较。与这两个最流行的抽样方案相比，LOS被证明提供了更高的收敛速度。LOS的实现只需要对现有的吉布斯采样码进行很小的修改。WIRESS Comput Stat 2013。DOI：10.1002/wics.1265
This article is a review article on the optimal Markov chain Monte Carlo (MCMC) sampling. The focus is on homogeneous Markov chains. This article first reviews the problem of finding the optimal transition matrix, which is defined to minimize the asymptotic variance of MCMC estimators. The article later reviews the locally optimal sampler (LOS), an MCMC sampling that performs local updates based on the optimal transition matrix. We conducted a simulation study to compare the LOS with the Metropolis–Hastings and the Gibbs Sampler. The LOS was shown to provide an improved rate of convergence over these two most popular sampling schemes. The implementation of the LOS requires only minor modifications in existing Gibbs sampling code. WIREs Comput Stat 2013. doi: 10.1002/wics.1265