Learning Bayesian networks with mixed variables

Susanne Bottcher
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:13-20, 2001.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR3-bottcher01a, title = {Learning Bayesian networks with mixed variables}, author = {Bottcher, Susanne}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {13--20}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/bottcher01a/bottcher01a.pdf}, url = {https://proceedings.mlr.press/r3/bottcher01a.html}, 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.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Learning Bayesian networks with mixed variables %A Susanne Bottcher %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-bottcher01a %I PMLR %P 13--20 %U https://proceedings.mlr.press/r3/bottcher01a.html %V R3 %X 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. %Z Reissued by PMLR on 31 March 2021.
APA
Bottcher, S.. (2001). Learning Bayesian networks with mixed variables. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:13-20 Available from https://proceedings.mlr.press/r3/bottcher01a.html. Reissued by PMLR on 31 March 2021.

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