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This page is available in English.
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Palestra 10 (sexta às 14h00) Bayesian Models On Event Trees And The Chain Event Graph. Jim Q Smith (The University of Warwick). Abstract: Chain Event Graphs (CEGs) provide a framework for modeling many asymmetric discrete problems graphically. They arise naturally from descriptions about how situations can unfold. They are also a much more general class of models than the discrete Bayesian Network - which is a simple special case of a CEG. Conditional independence interrogation, conjugate estimation and fast model selection procedures are all possible within this new class. Finally they provide a framework for expressing and exploring causal hypotheses in a much more flexible way than is possible using a Causal Bayesian Network. I will use a number of simple examples to illustrate the various ways this new class of graphical models can be used effectively. |
| email: isbra-bayes at ime.usp.br |
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CAPES-Proex Pós–graduação em Estatística-USP |
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