Sequential classification on partially ordered sets

Curtis Tatsuoka and Thomas Ferguson

Journal of the Royal Statistical Society Series B, 2003, vol. 65, issue 1, 143-157

Abstract: 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.

Date: 2003
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