Convergence and accuracy of Gibbs sampling; for conditional distributions in generalized linear models

• 成果类型: Article
• 署名作者: Kolassa, JE
• 署名单位: University of Rochester
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1018031104
• 发表日期: 1999
• 页码: 129-142
• 关键词: posterior distributions; monte-carlo; approximations

摘要

This paper presents convergence conditions for a Markov chain constructed using Gibbs sampling, when the equilibrium distribution is the conditional sampling distribution of sufficient statistics from a generalized Linear model. For cases when this unidimensional sampling is done approximately rather than exactly, the difference between the target equilibrium distribution and the resulting equilibrium distribution is expressed in terms of the difference between the true and approximating univariate conditional distributions. These methods are applied to an algorithm facilitating approximate conditional inference in canonical exponential families.