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Dettagli Pubblicazione
Autori:Daniele Codetta Raiteri
Luigi Portinale
Area Scientifica:Uncertain Reasoning
Probabilistic Graphical Models
Formal Models
Titolo:A GSPN Semantics for Continuous Time Bayesian Networks with Immediate Nodes
Apparso su:TR-INF-2009-03-03-UNIPMN
Editore:DiSIT, Computer Science Institute, UPO
Anno:2009
Tipo Pubblicazione:Technical Report
URL:http://www.di.unipmn.it...R-INF-2009-03-03-UNIPMN.pdf
Sommario:In this report we present an extension to Continuous Time Bayesian Networks (CTBN) called Generalized Continuous Time Bayesian Networks (GCTBN). The formalism allows one to model, in addition to continuous time delayed variables (with exponentially distributed transition rates), also non delayed or “immediate” variables, which acts as standard chance nodes in a Bayesian Network. This allows the modeling of processes having both a continuous-time temporal component and an immediate (i.e. non-delayed) component capturing the logical/probabilistic interactions among the model’s variables. The usefulness of this kind of model is discussed through an ex- ample concerning the reliability of a simple component-based system. A se- mantic model of GCTBNs, based on the formalism of Generalized Stochas- tic Petri Nets (GSPN). is outlined, whose purpose is twofold: to provide a well-defined semantics for GCTBNs in terms of the underlying stochastic process, and to provide an actual mean to perform inference (both predic- tion and smoothing) on GCTBNs. The example case study is then used, in order to highlight the exploitation of GSPN analysis for posterior probability computation on the GCTBN model.