DMCA
MINIMUM MEAN BAYES RISK ERROR QUANTIZATION OF PRIOR PROBABILITIES
| Citations: | 2 - 2 self |
BibTeX
@MISC{Varshney_minimummean,
author = {Kush R. Varshney and Lav R. Varshney},
title = {MINIMUM MEAN BAYES RISK ERROR QUANTIZATION OF PRIOR PROBABILITIES},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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. Index Terms — quantization, categorization, Bayesian hypothesis testing, signal detection, Bayes risk error
Keyphrases
bayesian hypothesis testing several experimental finding human decision making nearest neighbor centroid condition mean bayes risk error signal detection distortion measure bayes risk error index term quantization prior probability optimal quantization random vector hypothesis testing quantizer optimality quantized prior bayesian hypothesis testing
