DMCA
A Bayesian method for the induction of probabilistic networks from data (1992)
| Venue: | MACHINE LEARNING |
| Citations: | 1396 - 31 self |
BibTeX
@INPROCEEDINGS{Cooper92abayesian,
author = {Gregory F. Cooper and EDWARD HERSKOVITS},
title = {A Bayesian method for the induction of probabilistic networks from data},
booktitle = {MACHINE LEARNING},
year = {1992},
pages = {309--347},
publisher = {}
}
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Abstract
This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
Keyphrases
bayesian method probabilistic network open problem multiple belief network computer-assisted hypothesis testing basic method previous work preliminary evaluation bayesian belief network probabilistic inference scientific discovery probabilistic expert system potential application belief network
