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Quantization of Prior Probabilities for Hypothesis Testing (0805.4338v1)
Published 28 May 2008 in cs.IT, math.IT, math.ST, and stat.TH
Abstract: Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making in segregated populations is studied assuming Bayesian hypothesis testing with quantized priors.