Recursive Bayesian Nets for Prediction, Explanation and Control in Cancer Science
|Abstract||this paper we argue that the formalism can also be applied to modelling the hierarchical structure of physical 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 are vital for prediction, explanation and control respectively, a recursive Bayesian net can be applied to all these tasks. We show how a Recursive Bayesian Net can be used to model mechanisms in cancer science. The highest level of the proposed model will contain variables at the clinical level, while a middle level will map the structure of the DNA damage response mechanism and the lowest level will contain information about gene expression.|
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
|External links||This entry has no external links. Add one.|
|Through your library||Only published papers are available at libraries|
Similar books and articles
Jon Williamson (2011). Models for Prediction, Explanation and Control: Recursive Bayesian Networks. Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (70):5-33.
Lorenzo Casini, Phyllis Mckay Illari, Federica Russo & Jon Williamson (2011). Models for Prediction, Explanation and Control. Theoria 26 (1):5-33.
Matt Williams & Jon Williamson (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information 15 (1-2):155-178.
Jon Williamson (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information 15 (1-2):155-178.
Michael Strevens (2006). Scientific Explanation. In D. M. Borchert (ed.), Encyclopedia of Philosophy, second edition.
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.
Bénédicte Bes, Steven Sloman, Christopher G. Lucas & Éric Raufaste (2012). Non-Bayesian Inference: Causal Structure Trumps Correlation. Cognitive Science 36 (7):1178-1203.
Added to index2010-12-22
Total downloads10 ( #114,329 of 722,836 )
Recent downloads (6 months)1 ( #60,541 of 722,836 )
How can I increase my downloads?