Quantization of Prior Probabilities for Hypothesis Testing
Information Theory
2008-09-20 v1 math.IT
Statistics Theory
Statistics Theory
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.
Keywords
Cite
@article{arxiv.0805.4338,
title = {Quantization of Prior Probabilities for Hypothesis Testing},
author = {Kush R. Varshney and Lav R. Varshney},
journal= {arXiv preprint arXiv:0805.4338},
year = {2008}
}