Sequential Classification on Partially Ordered Sets

Curtis M. Tatsuoka

66 citations0/0 files storedJournal of the Royal Statistical Society Series B (Statistical Methodology)

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Optimal Experimental Design Methods Probability and Statistical Research Advanced Bandit Algorithms Research

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.

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1972Sequential Analysis and Optimal Design 1962Uncertainty, Information, and Sequential Experiments 2002Data Analytic Methods for Latent Partially Ordered Classification Models 1988A class of stochastic procedures for the assessment of knowledge 1987Bug Distribution and Statistical Pattern Classification

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