Partially collapsed Gibbs samplers: Theory and methods
成果类型：
Article
署名作者：
van Dyk, David A.; Park, Taeyoung
署名单位：
University of California System; University of California Irvine; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/016214508000000409
发表日期：
2008
页码：
790-796
关键词：
摘要：
Ever-increasing computational power, along with ever-more sophisticated statistical computing techniques, is making it possible to fit ever-more complex statistical models. Among the more computationally intensive methods, the Gibbs sampler is popular because of its simplicity and power to effectively generate samples from a high-dimensional probability distribution. Despite its simple implementation and description, however, the Gibbs sampler is criticized for its sometimes slow convergence, especially when it is used to fit highly structured complex models. Here we present partially collapsed Gibbs sampling strategies that improve the convergence by capitalizing on a set of functionally incompatible conditional distributions. Such incompatibility generally is avoided in the construction of a Gibbs sampler, because the resulting convergence properties are not well understood. We introduce three basic tools (marginalization, permutation, and trimming) that allow us to transform a Gibbs sampler into a partially collapsed Gibbs sampler with known stationary distribution and faster convergence.
来源URL：