Quantization of prior probabilities for hypothesis testing

Kush R. Varshney, Lav R. Varshney

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)4553-4562
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number10 I
StatePublished - 2008
Externally publishedYes


  • Bayes risk error
  • Bayesian hypothesis testing
  • Categorization
  • Classification
  • Detection
  • Quantization

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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