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Models for Prediction, Explanation and Control: Recursive Bayesian Networks

Casini, Lorenzo and Illari, Phyllis McKay and Russo, Federica and Williamson, Jon (2011) Models for Prediction, Explanation and Control: Recursive Bayesian Networks. THEORIA. An International Journal for Theory, History and Foundations of Science, 26 (1). pp. 5-33. ISSN 2171-679X

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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.

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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Casini, Lorenzol.casini@kent.ac.uk
Illari, Phyllis McKayp.mckay@kent.ac.uk
Russo, Federicaf.russo@kent.ac.uk
Williamson, Jonj.williamson@kent.ac.uk
Additional Information: ISSN: 0495-4548 (print)
Keywords: Bayesian network; causal model; mechanism; explanation; prediction; control
Depositing User: Dr Phyllis Illari
Date Deposited: 04 Feb 2014 23:29
Last Modified: 28 Sep 2018 15:57
Item ID: 10288
Journal or Publication Title: THEORIA. An International Journal for Theory, History and Foundations of Science
Publisher: Euskal Herriko Unibertsitatea / Universidad del País Vasco
Official URL: http://www.ehu.es/ojs/index.php/THEORIA/article/vi...
DOI or Unique Handle: 10.1387/theoria.784
Date: February 2011
Page Range: pp. 5-33
Volume: 26
Number: 1
ISSN: 2171-679X
URI: https://philsci-archive.pitt.edu/id/eprint/10288

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