Learning Bayesian Networks: A unification for discrete and Gaussian domains (1995)
| Venue: | PROCEEDINGS OF ELEVENTH CONFERENCE ON UNCERTAINTY INARTI CIAL INTELLIGENCE |
| Citations: | 36 - 5 self |
BibTeX
@INPROCEEDINGS{Heckerman95learningbayesian,
author = {David Heckerman and Dan Geiger},
title = {Learning Bayesian Networks: A unification for discrete and Gaussian domains},
booktitle = {PROCEEDINGS OF ELEVENTH CONFERENCE ON UNCERTAINTY INARTI CIAL INTELLIGENCE},
year = {1995},
publisher = {Morgan Kaufmann}
}
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Abstract
We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with well-known statistical facts about the Dirichlet and normal{Wishart distributions to derive our metrics for discrete and Gaussian domains.
