Exploring an Adaptive Metropolis Algorithm
Exploring an Adaptive Metropolis Algorithm
复制标题
探索自适应大都会算法
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Wells
中科院分区：
文献类型：
--
作者：
B. Shaby;M. Wells
虽然MCMC的自适应方法正在积极开发中，但其实用性尚未得到充分认识。我们简要回顾了自适应MCMC相关的一些理论成果。然后，我们提出了一个非常简单和有效的算法，以适应随机行走大都会和大都会调整Langevin算法的建议密度。该算法的好处是立竿见影的，我们证明了它的权力，通过比较其性能的三个常用的MCMC算法，被广泛认为是非常有效的。与概率单位模型的数据增强、地质统计模型的切片采样以及具有自适应拒绝采样的吉布斯采样相比，
While adaptive methods for MCMC are under active development, their utility has been under-recognized. We briefly review some theoretical results relevant to adaptive MCMC. We then suggest a very simple and effective algorithm to adapt proposal densities for random walk Metropolis and Metropolis adjusted Langevin algorithms. The benefits of this algorithm are immediate, and we demonstrate its power by comparing its performance to that of three commonly-used MCMC algorithms that are widely-believed to be extremely efficient. Compared to data augmentation for probit models, slice sampling for geostatistical models, and Gibbs sampling with adaptive rejection sampling,