ML Anthology

Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability

James Cussens
ECML-PKDD 1993 pp. 136-152
doi:10.1007/3-540-56602-3_133 /ecmlpkdd/1993/cussens1993ecml-bayes/

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.

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Cite

Text

Cussens. "Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability." European Conference on Machine Learning, 1993. doi:10.1007/3-540-56602-3_133

Markdown

[Cussens. "Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability." European Conference on Machine Learning, 1993.](https://mlanthology.org/ecmlpkdd/1993/cussens1993ecml-bayes/) doi:10.1007/3-540-56602-3_133

BibTeX

@inproceedings{cussens1993ecml-bayes,
  title     = {{Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability}},
  author    = {Cussens, James},
  booktitle = {European Conference on Machine Learning},
  year      = {1993},
  pages     = {136-152},
  doi       = {10.1007/3-540-56602-3_133},
  url       = {https://mlanthology.org/ecmlpkdd/1993/cussens1993ecml-bayes/}
}