|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|
|Keywords||No keywords specified (fix it)|
|Through your library||Only published papers are available at libraries|
Similar books and articles
Lorenzo Casini, Phyllis Mckay Illari, Federica Russo & Jon Williamson (2011). Models for Prediction, Explanation and Control. Theoria 26 (1):5-33.
Alexander Gebharter & Marie I. Kaiser (forthcoming). Causal Graphs and Biological Mechanisms. In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special science: The case of biology and history. Springer.
Donald Gillies (2002). Causality, Propensity, and Bayesian Networks. Synthese 132 (1-2):63 - 88.
Giuseppe Boccignone & Roberto Cordeschi, Bayesian Models and Simulations in Cognitive Science. Models and Simulations 2.
Michael Strevens (2006). Scientific Explanation. In D. M. Borchert (ed.), Encyclopedia of Philosophy, second edition.
Matt Williams & Jon Williamson (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information 15 (1-2).
Bénédicte Bes, Steven Sloman, Christopher G. Lucas & Éric Raufaste (2012). Non-Bayesian Inference: Causal Structure Trumps Correlation. Cognitive Science 36 (7):1178-1203.
Joseph F. Hanna (1969). Explanation, Prediction, Description, and Information Theory. Synthese 20 (3):308 - 334.
M. Colombo & P. Series (2012). Bayes in the Brain--On Bayesian Modelling in Neuroscience. British Journal for the Philosophy of Science 63 (3):697-723.
Added to index2010-11-26
Total downloads13 ( #88,037 of 549,744 )
Recent downloads (6 months)1 ( #63,425 of 549,744 )
How can I increase my downloads?