A Tight Upper Bound on the Bayesian Probability of Error

W. A. Hashlamoun, P. K. Varshney, V. N.S. Samarasooriya

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


In this paper, we present a new upper bound on the minimum probability of error of Bayesian decision systems. This new bound is continuous everywhere and is shown to be tighter than several existing bounds such as the Bhattacharyya and the Bayesian bounds. Numerical results are also presented.

Original languageEnglish (US)
Pages (from-to)220-224
Number of pages5
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number2
StatePublished - Feb 1994


  • Ali-Silvey distance measures
  • Bayesian decision systems
  • divergence
  • minimum probability of error
  • probability of error bounds
  • statistical pattern recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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