Multi-Entity Bayesian Networks without Multi-Tears

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dc.contributor.authorCosta, Paulo C. G.
dc.contributor.authorLaskey, Kathryn B.
dc.date.accessioned2006-01-27T23:47:24Z
dc.date.available2006-01-27T23:47:24Z
dc.date.issued2006-01-27T23:47:24Z
dc.description.abstractAn 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.extent4535781 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/1920/456
dc.language.isoen_US
dc.relation.ispartofseriesC4I-05-07
dc.subjectMulti-entity Bayesian networks
dc.subjectFirst-Order Bayesian Logic
dc.subjectProbabilistic Reasoning
dc.titleMulti-Entity Bayesian Networks without Multi-Tears
dc.typeWorking Paper

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