Scoring and Searching over Bayesian Networks with Causal and Associative Priors

Giorgos Borboudakis, Ioannis Tsamardinos
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:322-331, 2013.

Abstract

A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths in the true network. Such beliefs correspond to knowledge about the possible causal and associative relations between pairs of variables. This type of knowledge naturally arises from prior experimental and observational data, among others. In addition, a novel search-operator is proposed to take advantage of such prior knowledge. Experiments show that, using path beliefs improves the learning of the skeleton, as well as the edge directions in the network.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-borboudakis13a, title = {Scoring and Searching over {B}ayesian Networks with Causal and Associative Priors}, author = {Borboudakis, Giorgos and Tsamardinos, Ioannis}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {322--331}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/borboudakis13a/borboudakis13a.pdf}, url = {https://proceedings.mlr.press/r11/borboudakis13a.html}, abstract = {A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths in the true network. Such beliefs correspond to knowledge about the possible causal and associative relations between pairs of variables. This type of knowledge naturally arises from prior experimental and observational data, among others. In addition, a novel search-operator is proposed to take advantage of such prior knowledge. Experiments show that, using path beliefs improves the learning of the skeleton, as well as the edge directions in the network.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Scoring and Searching over Bayesian Networks with Causal and Associative Priors %A Giorgos Borboudakis %A Ioannis Tsamardinos %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-borboudakis13a %I PMLR %P 322--331 %U https://proceedings.mlr.press/r11/borboudakis13a.html %V R11 %X A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths in the true network. Such beliefs correspond to knowledge about the possible causal and associative relations between pairs of variables. This type of knowledge naturally arises from prior experimental and observational data, among others. In addition, a novel search-operator is proposed to take advantage of such prior knowledge. Experiments show that, using path beliefs improves the learning of the skeleton, as well as the edge directions in the network. %Z Reissued by PMLR on 04 October 2026.
APA
Borboudakis, G. & Tsamardinos, I.. (2013). Scoring and Searching over Bayesian Networks with Causal and Associative Priors. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:322-331 Available from https://proceedings.mlr.press/r11/borboudakis13a.html. Reissued by PMLR on 04 October 2026.

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