Optimal scaling of the independence sampler: Theory and practice
Optimal scaling of the independence sampler: Theory and practice
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DOI：
10.3150/16-bej908
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发表时间：
2018-08-01
期刊：
影响因子：
1.5
通讯作者：
Neal, Peter
中科院分区：
文献类型：
--
作者：
Lee, Clement;Neal, Peter
独立采样器是最常用的MCMC算法之一，通常作为Metropolisp-withinp-Gibbs算法的组成部分。独立抽样者的共同焦点是选择建议分布以获得尽可能高的接受率。在本文中，我们有一个有点不同的重点集中在使用的独立采样器更新增强数据的贝叶斯框架中存在的独立采样器的自然建议分布。因此，我们集中在比例的增广数据更新，以优化独立采样器。通用的指导方针，优化独立采样器获得独立和相同分布的产品密度镜像随机游走大都会算法的调查结果。通用的指导方针是翔实的理想化的产品密度在两个流行病的例子超出了狭窄的范围。
The independence sampler is one of the most commonly used MCMC algorithms usually as a component of a Metropolisp-withinp-Gibbs algorithm. The common focus for the independence sampler is on the choice of proposal distribution to obtain an as high as possible acceptance rate. In this paper, we have a somewhat different focus concentrating on the use of the independence sampler for updating augmented data in a Bayesian framework where a natural proposal distribution for the independence sampler exists. Thus, we concentrate on the proportion of the augmented data to update to optimise the independence sampler. Generic guidelines for optimising the independence sampler are obtained for independent and identically distributed product densities mirroring findings for the random walk Metropolis algorithm. The generic guidelines are shown to be informative beyond the narrow confines of idealised product densities in two epidemic examples.