Quantization of Prior Probabilities for Hypothesis Testing

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dc.creatorVarshney, Kush R.
dc.creatorVarshney, Lav R.
dc.date2008-05-28
dc.date.accessioned2026-07-07T10:03:37Z
dc.date.available2026-07-07T10:03:37Z
dc.descriptionBayesian 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.
dc.identifierhttps://arxiv.org/abs/0805.4338
dc.identifierhttp://arxiv.org/abs/0805.4338
dc.identifierIEEE Transactions on Signal Processing, vol. 56, no. 10, October 2008, p. 4553-4562
dc.identifierdoi:10.1109/TSP.2008.928164
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/169353
dc.subjectInformation Theory
dc.subjectStatistics Theory
dc.titleQuantization of Prior Probabilities for Hypothesis Testing
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