Efficient Sampling Using Metropolis Algorithms: Applications of Optimal Scaling Results
Efficient Sampling Using Metropolis Algorithms: Applications of Optimal Scaling Results
复制标题
使用 Metropolis 算法进行高效采样：最佳缩放结果的应用
DOI：
10.1198/108571108x319970
复制
发表时间：
2008
影响因子：
2.4
通讯作者：
M. Bédard
中科院分区：
文献类型：
--
作者：
M. Bédard
我们最近考虑了具有非 IID 分量的多维目标分布的 Metropolis 算法的最优缩放问题。已证明的结果具有广泛的应用，本文的目的是展示从业者如何利用它们。特别是，我们使用几个例子来说明渐近最优接受率为通常的 0.234 的情况，以及最新的发展，应采用较小的接受率从所涉及的目标分布中进行最佳采样。我们研究了提案缩放对算法性能的影响，最后进行模拟研究，探索从一些流行的统计模型中采样时算法的效率。
We recently considered the optimal scaling problem of Metropolis algorithms for multidimensional target distributions with non-IID components. The results that were proven have wide applications and the aim of this article is to show how practitioners can take advantage of them. In particular, we use several examples to illustrate the casewhere the asymptotically optimal acceptance rate is the usual 0.234, and also the latest developments where smaller acceptance rates should be adopted for optimal sampling from the target distributions involved. We study the impact of the proposal scaling on the performance of the algorithm, and finally perform simulation studies exploring the efficiency of the algorithm when sampling from some popular statistical models.