University of Hertfordshire

Models for Prediction, Explanation and Control: Recursive Bayesian Nets

Research output: Contribution to journalArticle


  • 904658

    Final published version, 426 KB, PDF document


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


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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