Models for Prediction, Explanation and Control: Recursive Bayesian Networks

The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilit...

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Bibliographic Details
Published in:Theoria (Madrid, Spain) Spain), 2011-01, Vol.26 (1(70)), p.5-33
Main Authors: Casini, Lorenzo, Illari, Phyllis McKay, Russo, Federica, Williamson, Jon
Format: Article
Language:English
Subjects:
Apoptosis
Bayesian network
Bayesian networks
Causal model
Causality
Control
Daughter cells
DNA
DNA damage
Explanation
Mechanism
Modeling
Mother cells
Multilevel models
Prediction
Probabilistic modeling
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Summary:The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis.
ISSN:0495-4548
2171-679X
DOI:10.1387/theoria.784