University of Hertfordshire

Models for Prediction, Explanation and Control: Recursive Bayesian Nets

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  • Phyllis Illari
  • Lorenzo Casini
  • Federica Russo
  • Jon Williamson
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Original languageEnglish
Pages (from-to)5-33
JournalTHEORIA
Volume26
Issue1
Publication statusPublished - 2011

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.

Notes

THEORIA is a non-profit editorial venture. It is published by the University of the Basque Country under a Creative Commons Licence. It is part of the Open Journal System (OJS) and all its papers (from 2003 onwards) are freely available on-line.

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