Algebraic algorithms for sampling from conditional distributions

• 成果类型: Article
• 署名作者: Diaconis, P; Sturmfels, B
• 署名单位: Cornell University; University of California System; University of California Berkeley
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1998
• 页码: 363-397
• 关键词: of-fit statistics; contingency-tables; buchberger algorithm; markov-chains; tests; inference; regression; models

摘要

We construct Markov chain algorithms for sampling from discrete exponential families conditional on a sufficient statistic. Examples include contingency tables, logistic regression, and spectral analysis of permutation data. The algorithms involve computations in polynomial rings using Grobner bases.