Bayesian methods for partial stochastic orderings

• 成果类型: Article
• 署名作者: Hoff, PD
• 署名单位: University of Washington; University of Washington Seattle
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/90.2.303
• 发表日期: 2003
• 页码: 303317
• 关键词: nonparametric maximum-likelihood; normalizing constants; distributions; dependence; inference

摘要

We discuss two methods of making nonparametric Bayesian inference on probability measures subject to a partial stochastic ordering. The first method involves a nonparametric prior for a measure on partially ordered latent observations, and the second involves rejection sampling. Computational approaches are discussed for each method, and interpretations of prior and posterior information are discussed. An application is presented in which inference is made on the number of independently segregating quantitative trait loci present in an animal population.