Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Sequential Classification on Partially Ordered Sets

Sequential Classification on Partially Ordered Sets SummaryA 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Royal Statistical Society Series B (Statistical Methodology) Oxford University Press

Loading next page...
 
/lp/oxford-university-press/sequential-classification-on-partially-ordered-sets-4tgWyF6VmT

References (25)

Copyright
© 2003 Royal Statistical Society
ISSN
1369-7412
eISSN
1467-9868
DOI
10.1111/1467-9868.00377
Publisher site
See Article on Publisher Site

Abstract

SummaryA 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.

Journal

Journal of the Royal Statistical Society Series B (Statistical Methodology)Oxford University Press

Published: Jan 28, 2003

Keywords: Cognitive diagnosis; Group testing; Kullback–Leibler information; Optimal rates of convergence; Partially ordered set; Sequential selection of experiments

There are no references for this article.