On Intercausal Interactions in Probabilistic Relational Models

S. Renooij; L.C. van der Gaag; Ph. Leray

On Intercausal Interactions in Probabilistic Relational Models

S. Renooij
, L.C. van der Gaag
, Ph. Leray

Université de Nantes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Probabilistic relational models (PRMs) extend Bayesian networks beyond propositional expressiveness by allowing the representation of multiple interacting
classes. For a specific instance of sets of concrete objects per class, a ground Bayesian network is composed by replicating parts of the PRM. The interactions between the objects that are thereby induced, are not always obvious from the PRM. We demonstrate in this paper that the replicative structure of the ground
network in fact constrains the space of possible probability distributions and thereby the possible patterns of intercausal interaction

Original language	English
Title of host publication	International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium
Editors	Jasper De Bock, Cassio P. de Campos, Gert de Cooman, Erik Quaeghebeur, Gregory Wheeler
Publisher	MLResearchPress
Pages	327-329
Number of pages	3
Publication status	Published - 2019
Event	International Symposium on Imprecise Probabilities: Theories and Applications - Ghent, Belgium Duration: 3 Jul 2019 → 6 Jul 2019 Conference number: 11

Publication series

Name	Proceedings of Machine Learning Research
Publisher	MLResearchPress
Volume	103

Conference

Conference	International Symposium on Imprecise Probabilities: Theories and Applications
Abbreviated title	ISIPTA
Country/Territory	Belgium
City	Ghent
Period	3/07/19 → 6/07/19

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Renooij, S., van der Gaag, L. C., & Leray, P. (2019). On Intercausal Interactions in Probabilistic Relational Models. In J. De Bock, C. P. de Campos, G. de Cooman, E. Quaeghebeur, & G. Wheeler (Eds.), International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium (pp. 327-329). (Proceedings of Machine Learning Research; Vol. 103). MLResearchPress. http://proceedings.mlr.press/v103/renooij19a.html

Renooij, S. ; van der Gaag, L.C. ; Leray, Ph. / On Intercausal Interactions in Probabilistic Relational Models. International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium. editor / Jasper De Bock ; Cassio P. de Campos ; Gert de Cooman ; Erik Quaeghebeur ; Gregory Wheeler. MLResearchPress, 2019. pp. 327-329 (Proceedings of Machine Learning Research).

@inproceedings{26407ba0b8fc44c9b31ac35809985df7,

title = "On Intercausal Interactions in Probabilistic Relational Models",

abstract = "Probabilistic relational models (PRMs) extend Bayesian networks beyond propositional expressiveness by allowing the representation of multiple interacting classes. For a specific instance of sets of concrete objects per class, a ground Bayesian network is composed by replicating parts of the PRM. The interactions between the objects that are thereby induced, are not always obvious from the PRM. We demonstrate in this paper that the replicative structure of the ground network in fact constrains the space of possible probability distributions and thereby the possible patterns of intercausal interaction",

author = "S. Renooij and \{van der Gaag\}, L.C. and Ph. Leray",

year = "2019",

language = "English",

series = "Proceedings of Machine Learning Research",

publisher = "MLResearchPress",

pages = "327--329",

editor = "\{ De Bock\}, Jasper and \{de Campos\}, \{Cassio P.\} and \{de Cooman\}, \{Gert \} and \{ Quaeghebeur\}, Erik and \{ Wheeler\}, Gregory",

booktitle = "International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium",

note = "International Symposium on Imprecise Probabilities: Theories and Applications , ISIPTA ; Conference date: 03-07-2019 Through 06-07-2019",

}

Renooij, S, van der Gaag, LC & Leray, P 2019, On Intercausal Interactions in Probabilistic Relational Models. in J De Bock, CP de Campos, G de Cooman, E Quaeghebeur & G Wheeler (eds), International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium. Proceedings of Machine Learning Research, vol. 103, MLResearchPress, pp. 327-329, International Symposium on Imprecise Probabilities: Theories and Applications , Ghent, Belgium, 3/07/19. <http://proceedings.mlr.press/v103/renooij19a.html>

On Intercausal Interactions in Probabilistic Relational Models. / Renooij, S.; van der Gaag, L.C.; Leray, Ph.
International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium. ed. / Jasper De Bock; Cassio P. de Campos; Gert de Cooman; Erik Quaeghebeur; Gregory Wheeler. MLResearchPress, 2019. p. 327-329 (Proceedings of Machine Learning Research; Vol. 103).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - On Intercausal Interactions in Probabilistic Relational Models

AU - Renooij, S.

AU - van der Gaag, L.C.

AU - Leray, Ph.

N1 - Conference code: 11

PY - 2019

Y1 - 2019

N2 - Probabilistic relational models (PRMs) extend Bayesian networks beyond propositional expressiveness by allowing the representation of multiple interacting classes. For a specific instance of sets of concrete objects per class, a ground Bayesian network is composed by replicating parts of the PRM. The interactions between the objects that are thereby induced, are not always obvious from the PRM. We demonstrate in this paper that the replicative structure of the ground network in fact constrains the space of possible probability distributions and thereby the possible patterns of intercausal interaction

AB - Probabilistic relational models (PRMs) extend Bayesian networks beyond propositional expressiveness by allowing the representation of multiple interacting classes. For a specific instance of sets of concrete objects per class, a ground Bayesian network is composed by replicating parts of the PRM. The interactions between the objects that are thereby induced, are not always obvious from the PRM. We demonstrate in this paper that the replicative structure of the ground network in fact constrains the space of possible probability distributions and thereby the possible patterns of intercausal interaction

M3 - Conference contribution

T3 - Proceedings of Machine Learning Research

SP - 327

EP - 329

BT - International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium

A2 - De Bock, Jasper

A2 - de Campos, Cassio P.

A2 - de Cooman, Gert

A2 - Quaeghebeur, Erik

A2 - Wheeler, Gregory

PB - MLResearchPress

T2 - International Symposium on Imprecise Probabilities: Theories and Applications

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Renooij S, van der Gaag LC, Leray P. On Intercausal Interactions in Probabilistic Relational Models. In De Bock J, de Campos CP, de Cooman G, Quaeghebeur E, Wheeler G, editors, International Symposium on Imprecise Probabilities: Theories and Applications, 3-6 July 2019, Thagaste, Ghent, Belgium. MLResearchPress. 2019. p. 327-329. (Proceedings of Machine Learning Research).

On Intercausal Interactions in Probabilistic Relational Models

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