Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability
Lecture notes in computer sciencePublished 1 January 1993Open access
James Cussens
Citations35
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
Various ways of estimating probabilities, mainly within the Bayesian framework, are discussed and their relevance and application to machine learning is given, and their relative performance empirically evaluated.
Abstract
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.
Keywords
Computer Science
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