Bayes and pseudo-Bayes estimates of conditional probability and their reliability

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Various ways of estimating probabilities, mainly within the Bayesian framework, are discussed. Their relevance and application to machine learning is given, and their relative performance empirically evaluated. A method of accounting for noisy data is given and also applied. The reliability of estimates is measured by a significance measure, which is also empirically tested. We briefly discuss the use of likelihood ratio as a significance measure.
Original languageEnglish
Title of host publicationMachine Learning: ECML-93
EditorsPavel B. Brazdil
Number of pages17
Publication statusPublished - 1993

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