ML Anthology

Learning Bayesian Networks with Mixed Variables

Susanne Bottcher
AISTATS 2001 pp. 13-20
/aistats/2001/bottcher2001aistats-learning/

Abstract

The paper considers conditional Gaussian networks. As conjugate local priors, we use the Dirichlet distribution for discrete variables and the Gaussian-inverse Gamma distribution for continuous variables, given a configuration of the discrete parents. We assume parameter independence and complete data. Further, the network-score is calculated. We then develop a local master prior procedure, for deriving parameter priors in CG networks. The local master procedure satisfies parameter independence, parameter modularity and likelihood equivalence.

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Text

Bottcher. "Learning Bayesian Networks with Mixed Variables." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.

Markdown

[Bottcher. "Learning Bayesian Networks with Mixed Variables." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/bottcher2001aistats-learning/)

BibTeX

@inproceedings{bottcher2001aistats-learning,
  title     = {{Learning Bayesian Networks with Mixed Variables}},
  author    = {Bottcher, Susanne},
  booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics},
  year      = {2001},
  pages     = {13-20},
  volume    = {R3},
  url       = {https://mlanthology.org/aistats/2001/bottcher2001aistats-learning/}
}