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Multi-Entity Bayesian Networks without Multi-Tears

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dc.contributor.author Costa, Paulo C. G.
dc.contributor.author Laskey, Kathryn B.
dc.date.accessioned 2006-01-27T23:47:24Z
dc.date.available 2006-01-27T23:47:24Z
dc.date.issued 2006-01-27T23:47:24Z
dc.identifier.uri https://hdl.handle.net/1920/456
dc.description.abstract An introduction is provided to Multi-Entity Bayesian Networks (MEBN), a logic system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures. Knowledge is encoded as a collection of Bayesian network fragments (MFrags) that can be instantiated and combined to form highly complex situation-specific Bayesian networks. A MEBN theory (MTheory) implicitly represents a joint probability distribution over possibly unbounded numbers of hypotheses, and uses Bayesian learning to refine a knowledge base as observations accrue. MEBN provides a logical foundation for the emerging collection of highly expressive probability-based languages. A running example illustrates the representation and reasoning power of the MEBN formalism.
dc.format.extent 4535781 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.relation.ispartofseries C4I-05-07
dc.subject multi-entity Bayesian networks en_US
dc.subject First-Order Bayesian Logic en_US
dc.subject Probabilistic Reasoning en_US
dc.title Multi-Entity Bayesian Networks without Multi-Tears en
dc.type Working Paper en


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