Building Causal Interaction Models by Recursive Unfolding

L. C. van der Gaag, S. Renooij, A. Facchini
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:509-520, 2020.

Abstract

Causal interaction models, such as the well-known noisy-or and leaky noisy-or models, have become quite popular as a means to parameterize conditional probability tables for Bayesian networks. In this paper we focus on the engineering of subnetworks to represent such models and present a novel technique called recursive unfolding for this purpose. This technique allows inserting, removing and merging cause variables in an interaction model at will, without affecting the underlying represented information. We detail the technique, with the recursion invariants involved, and illustrate its practical use for Bayesian-network engineering by means of a small example.

Cite this Paper


BibTeX
@InProceedings{pmlr-v138-van-der-gaag20a, title = {Building Causal Interaction Models by Recursive Unfolding}, author = {{van der Gaag}, L. C. and Renooij, S. and Facchini, A.}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {509--520}, year = {2020}, editor = {Manfred Jaeger and Thomas Dyhre Nielsen}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/van-der-gaag20a/van-der-gaag20a.pdf}, url = { http://proceedings.mlr.press/v138/van-der-gaag20a.html }, abstract = {Causal interaction models, such as the well-known noisy-or and leaky noisy-or models, have become quite popular as a means to parameterize conditional probability tables for Bayesian networks. In this paper we focus on the engineering of subnetworks to represent such models and present a novel technique called recursive unfolding for this purpose. This technique allows inserting, removing and merging cause variables in an interaction model at will, without affecting the underlying represented information. We detail the technique, with the recursion invariants involved, and illustrate its practical use for Bayesian-network engineering by means of a small example.} }
Endnote
%0 Conference Paper %T Building Causal Interaction Models by Recursive Unfolding %A L. C. van der Gaag %A S. Renooij %A A. Facchini %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-van-der-gaag20a %I PMLR %P 509--520 %U http://proceedings.mlr.press/v138/van-der-gaag20a.html %V 138 %X Causal interaction models, such as the well-known noisy-or and leaky noisy-or models, have become quite popular as a means to parameterize conditional probability tables for Bayesian networks. In this paper we focus on the engineering of subnetworks to represent such models and present a novel technique called recursive unfolding for this purpose. This technique allows inserting, removing and merging cause variables in an interaction model at will, without affecting the underlying represented information. We detail the technique, with the recursion invariants involved, and illustrate its practical use for Bayesian-network engineering by means of a small example.
APA
van der Gaag, L.C., Renooij, S. & Facchini, A.. (2020). Building Causal Interaction Models by Recursive Unfolding. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:509-520 Available from http://proceedings.mlr.press/v138/van-der-gaag20a.html .

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