Sequential classification on partially ordered sets
成果类型：
Article
署名作者：
Tatsuoka, C; Ferguson, T
署名单位：
George Washington University; University of California System; University of California Los Angeles
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1111/1467-9868.00377
发表日期：
2003
页码：
143-157
关键词：
摘要：
A general theorem on the asymptotically optimal sequential selection of experiments is presented and applied to a Bayesian classification problem when the parameter space is a finite partially ordered set. The main results include establishing conditions under which the posterior probability of the true state converges to 1 almost surely and determining optimal rates of convergence. Properties of a class of experiment selection rules are explored.
来源URL：