Quantization of Prior Probabilities for Hypothesis Testing

Abstract--In this paper, 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 (MBRE) 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.

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Kush R. Varshney, Lav R. Varshney
• Keywords: Bayesian Hypothesis, Bayesian Hypothesis Testing, CORR 2008, Education, Prior Probabilities