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Arbitrarily Tight Upper and Lower Bounds on the Bayesian Probability of Error
January 1996 (vol. 18 no. 1)
pp. 89-91

Abstract—This paper presents new upper and lower bounds on the minimum probability of error of Bayesian decision systems for the two-class problem. These bounds can be made arbitrarily close to the exact minimum probability of error, making them tighter than any previously known bounds.

[1] P.A. Devijver, "On a new class of bounds on Bayes risk in multi-hypothesis pattern recognition," IEEE Trans. Computers, vol. 23, pp. 70-80, Jan. 1974.
[2] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis.New York: John Wiley and Sons, 1973.
[3] W.A. Hashlamoun, P.K. Varshney, and V.N.S. Samarasooriya, "A tight upper bound on the bayesian probability of error," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, pp. 220-224, Feb. 1994.
[4] M.E. Hellman and J. Raviv, "Probability of error, equivocation, and Chernoff bound," IEEE Trans. Information Theory, vol. 16, July 1970.
[5] T. Kailath, The Divergence and Bhattacharyya Distance Measures in Signal Selection IEEE Trans. Comm. Technology, vol. 15, no. 1, pp. 52-60, Feb. 1967

Index Terms:
Bayesian decision, probability of error, statistical pattern recognition.
Hadar Avi-Itzhak, Thanh Diep, "Arbitrarily Tight Upper and Lower Bounds on the Bayesian Probability of Error," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 1, pp. 89-91, Jan. 1996, doi:10.1109/34.476017
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