Minimum mean bayes risk error quantization of prior probabilities
Kush R. Varshney, Lav R. Varshney · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, must be quantized. Nearest neighbor and centroid conditions for quantizer optimality are derived using mean Bayes risk error as a distortion measure. An example of optimal quantization for hypothesis testing is provided. Human decision making is briefly studied assuming quantized prior Bayesian hypothesis testing; this model explains several experimental findings.