Sequential classification on partially ordered sets | Zendy

Tatsuoka Curtis | Zendy; Ferguson Thomas | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Sequential classification on partially ordered sets

Author(s) -

Tatsuoka Curtis,

Ferguson Thomas

Publication year - 2003

Publication title -

journal of the royal statistical society: series b (statistical methodology)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.523

H-Index - 137

eISSN - 1467-9868

pISSN - 1369-7412

DOI - 10.1111/1467-9868.00377

Subject(s) - selection (genetic algorithm) , mathematics , convergence (economics) , class (philosophy) , bayesian probability , finite set , set (abstract data type) , asymptotically optimal algorithm , state space , space (punctuation) , mathematical optimization , bayes' theorem , posterior probability , computer science , artificial intelligence , statistics , mathematical analysis , economics , programming language , economic growth , operating system

Summary. 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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research