Casini, L. and Illari, P. and Russo, F. and Williamson, J. (2011) Models for prediction, explanation and control: recursive Bayesian networks. Theoria, 26 (1). pp. 5-33. ISSN 0495-4548.
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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.
|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:||27 Jun 2012 09:39|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/26327 (The current URI for this page, for reference purposes)|
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