Mateescu, Robert and Dechter, Rina (2008) Mixed deterministic and probabilistic networks. Annals of Mathematics and Artificial Intelligence, 54 (1-3). pp. 3-51. ISSN 1012-2443 http://resolver.caltech.edu/CaltechAUTHORS:20090929-151329294
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The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are combined, achieving computational efficiency, semantic coherence and user-interface convenience. We define the semantics and graphical representation of mixed networks, and discuss the two main types of algorithms for processing them: inference-based and search-based. A preliminary experimental evaluation shows the benefits of the new model.
|Additional Information:||© 2009 Springer Science + Business Media B.V. Received: 19 March 2009. Accepted: 19 March 2009. Published online: 25 April 2009. This work was supported in part by the NSF grant IIS-0713118 and by the NIH grant R01-HG004175-02.|
|Subject Keywords:||Mixed network; Probabilistic information; Deterministic information; Graphical models; Automated reasoning; Inference; Search; AND/OR search|
|Classification Code:||Mathematics Subject Classifications (2000) 68T30 - 68T37 - 68T20 - 62F30 - 62F15.|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||09 Oct 2009 16:44|
|Last Modified:||26 Dec 2012 11:26|
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