Quantization of prior probabilities for hypothesis testing

Kush R. Varshney; Lav R. Varshney

doi:10.1109/TSP.2008.928164

Quantization of prior probabilities for hypothesis testing

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, 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 (MBRE) 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.

Original language	English (US)
Pages (from-to)	4553-4562
Number of pages	10
Journal	IEEE Transactions on Signal Processing
Volume	56
Issue number	10 I
DOIs	https://doi.org/10.1109/TSP.2008.928164
State	Published - 2008
Externally published	Yes

Keywords

Bayes risk error
Bayesian hypothesis testing
Categorization
Classification
Detection
Quantization

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

Online availability

10.1109/TSP.2008.928164

Library availability

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Cite this

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abstract = "In this paper, 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 (MBRE) 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 = "Bayes risk error, Bayesian hypothesis testing, Categorization, Classification, Detection, Quantization",

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doi = "10.1109/TSP.2008.928164",

language = "English (US)",

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Y1 - 2008

N2 - In this paper, 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 (MBRE) 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.

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KW - Bayesian hypothesis testing

KW - Categorization

KW - Classification

KW - Detection

KW - Quantization

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Quantization of prior probabilities for hypothesis testing

Abstract

Keywords

ASJC Scopus subject areas

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