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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.

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Index Terms:
Bayesian decision, probability of error, statistical pattern recognition.
Citation:
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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