Models for prediction, explanation and control: recursive Bayesian networks

Casini, Lorenzo and Illari, Phyllis and Russo, Frederica and Williamson, Jon (2011) Models for prediction, explanation and control: recursive Bayesian networks. Theoria, 26 (1). pp. 5-33. ISSN 0495-4548. (The full text of this publication is not available from this repository)

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Abstract

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.

Item Type: Article
Subjects: Q Science > Q Science (General)
B Philosophy. Psychology. Religion > B Philosophy (General)
Q Science > QP Physiology (Living systems) > QP506 Molecular biology
Divisions: Faculties > Humanities > School of European Culture and Languages
Depositing User: Jon Williamson
Date Deposited: 11 Feb 2011 16:56
Last Modified: 09 Apr 2014 15:45
Resource URI: http://kar.kent.ac.uk/id/eprint/26327 (The current URI for this page, for reference purposes)
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