@inproceedings{c5009dcdfe8443b59f0ac0de6e0116b6,
title = "Minimum mean Bayes risk error quantization of prior probabilities",
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.",
keywords = "Bayes risk error, Bayesian hypothesis testing, Categorization, Quantization, Signal detection",
author = "Varshney, \{Kush R.\} and Varshney, \{Lav R.\}",
year = "2008",
doi = "10.1109/ICASSP.2008.4518392",
language = "English",
isbn = "1424414849",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "3445--3448",
booktitle = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP",
note = "2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ; Conference date: 31-03-2008 Through 04-04-2008",
}