Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
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.
Cite
Text
Heckerman and Geiger. "Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains." Conference on Uncertainty in Artificial Intelligence, 1995.Markdown
[Heckerman and Geiger. "Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains." Conference on Uncertainty in Artificial Intelligence, 1995.](https://mlanthology.org/uai/1995/heckerman1995uai-learning/)BibTeX
@inproceedings{heckerman1995uai-learning,
title = {{Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains}},
author = {Heckerman, David and Geiger, Dan},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1995},
pages = {274-284},
url = {https://mlanthology.org/uai/1995/heckerman1995uai-learning/}
}