Abstract
We study two of the classical bounds for the Bayes error P e , Lissack and Fu’s separability bounds and Bhattacharyya’s bounds, in the classification of an observation into one of the two determined distributions, under the hypothesis that the prior probability χ itself has a probability distribution. The effectiveness of this distribution can be measured in terms of the ratio of two mean values. On the other hand, a discriminant analysis-based optimal classification rule allows us to derive the posterior distribution of χ, together with the related posterior bounds of P e .
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Research partially supported by NSERC grant A 9249 (Canada). The authors wish to thank two referees, for their very pertinent comments and suggestions, that have helped to improve the quality and the presentation of the paper, and we have, whenever possible, addressed their concerns.
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Pham-Gia, T., Turkkan, N. & Bekker, A. Bounds for the Bayes Error in Classification: A Bayesian Approach Using Discriminant Analysis. Stat. Meth. & Appl. 16, 7–26 (2007). https://doi.org/10.1007/s10260-006-0012-x
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DOI: https://doi.org/10.1007/s10260-006-0012-x