Transdimensional Markov chains: A decade of progress and future perspectives

• 成果类型: Review
• 署名作者: Sisson, SA
• 署名单位: University of New South Wales Sydney
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000000664
• 发表日期: 2005
• 页码: 1077-1089
• 关键词: reversible jump mcmc; monte-carlo; model selection; bayesian model; convergence assessment; normalizing constants; marginal likelihood; variable selection; unknown number; segmentation

摘要

The last 10 years have witnessed the development of sampling frameworks that permit the construction of Markov chains that simultaneously traverse both parameter and model space. Substantial methodological progress has been made during this period. In this article we present a survey of the current state of the art and evaluate some of the most recent advances in this field. We also discuss future research perspectives in the context of the drive to develop sampling mechanisms with high degrees of both efficiency and automation.