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Sequential classification on partially ordered sets

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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.

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

Document Type: Research Article


Publication date: 2003-02-01

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