SEQUENTIAL CLASSIFICATION ON PARTIALLY ORDERED SETS
Curtis Tatsuoka and Thomas Ferguson
The George Washington University, and
University of California at Los Angeles
Abstract
A general theorem on 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 one almost surely, and determining optimal rates of
convergence. Properties of various classes of experiment selection
rules are explored.