Modelling the interactions between discrete and continuous causal factors in bayesian networks

Peter J.F. Lucas; Arjen Hommersom

Modelling the interactions between discrete and continuous causal factors in bayesian networks

Peter J.F. Lucas^*
, Arjen Hommersom

^*Corresponding author for this work

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

2 Citations (Scopus)

3 Downloads (Pure)

Abstract

The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modelled by means of causal independence theory, as a theory of continuous causal independence was not available. In this paper, such a theory is developed and generalised such that it allows merging continuous with discrete parameters based on the characteristics of the problem at hand. This new theory is based on the discovered relationship between the theory of causal independence and convolution in probability theory, discussed for the first time in this paper. It is also illustrated how this new theory can be used in connection with special probability distributions.

Original language	English
Title of host publication	Proceedings of the 5th European Workshop on Probabilistic Graphical Models, PGM 2010
Pages	185-192
Number of pages	8
Publication status	Published - 2010
Externally published	Yes
Event	5th European Workshop on Probabilistic Graphical Models, PGM 2010 - Helsinki, Finland Duration: 13 Sept 2010 → 15 Sept 2010 Conference number: 5

Conference

Conference	5th European Workshop on Probabilistic Graphical Models, PGM 2010
Abbreviated title	PGM 2010
Country/Territory	Finland
City	Helsinki
Period	13/09/10 → 15/09/10

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title = "Modelling the interactions between discrete and continuous causal factors in bayesian networks",

abstract = "The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modelled by means of causal independence theory, as a theory of continuous causal independence was not available. In this paper, such a theory is developed and generalised such that it allows merging continuous with discrete parameters based on the characteristics of the problem at hand. This new theory is based on the discovered relationship between the theory of causal independence and convolution in probability theory, discussed for the first time in this paper. It is also illustrated how this new theory can be used in connection with special probability distributions.",

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booktitle = "Proceedings of the 5th European Workshop on Probabilistic Graphical Models, PGM 2010",

note = "5th European Workshop on Probabilistic Graphical Models, PGM 2010, PGM 2010 ; Conference date: 13-09-2010 Through 15-09-2010",

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T1 - Modelling the interactions between discrete and continuous causal factors in bayesian networks

AU - Lucas, Peter J.F.

AU - Hommersom, Arjen

N1 - Conference code: 5

PY - 2010

Y1 - 2010

N2 - The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modelled by means of causal independence theory, as a theory of continuous causal independence was not available. In this paper, such a theory is developed and generalised such that it allows merging continuous with discrete parameters based on the characteristics of the problem at hand. This new theory is based on the discovered relationship between the theory of causal independence and convolution in probability theory, discussed for the first time in this paper. It is also illustrated how this new theory can be used in connection with special probability distributions.

AB - The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modelled by means of causal independence theory, as a theory of continuous causal independence was not available. In this paper, such a theory is developed and generalised such that it allows merging continuous with discrete parameters based on the characteristics of the problem at hand. This new theory is based on the discovered relationship between the theory of causal independence and convolution in probability theory, discussed for the first time in this paper. It is also illustrated how this new theory can be used in connection with special probability distributions.

UR - https://www.scopus.com/pages/publications/84874678282

M3 - Conference contribution

AN - SCOPUS:84874678282

SN - 9789526033143

SP - 185

EP - 192

BT - Proceedings of the 5th European Workshop on Probabilistic Graphical Models, PGM 2010

T2 - 5th European Workshop on Probabilistic Graphical Models, PGM 2010

Y2 - 13 September 2010 through 15 September 2010

ER -

Modelling the interactions between discrete and continuous causal factors in bayesian networks

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