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

James Cussens

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Machine Learning: ECML-93
Editors	Pavel B. Brazdil
Publisher	Springer
Pages	136-152
Number of pages	17
Volume	667
Publication status	Published - 1993

Publication series

Name	LNAI

Access to Document

ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/ecml93.ps.gz

Cite this

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title = "Bayes and pseudo-Bayes estimates of conditional probability and their reliability",

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.",

author = "James Cussens",

year = "1993",

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editor = "Brazdil, {Pavel B.}",

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AB - 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.

M3 - Conference contribution

VL - 667

T3 - LNAI

SP - 136

EP - 152

BT - Machine Learning: ECML-93

A2 - Brazdil, Pavel B.

PB - Springer

ER -