Multi-Entity Bayesian Networks without Multi-Tears
Date
2006-01-27T23:47:24Z
Authors
Costa, Paulo C. G.
Laskey, Kathryn B.
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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.
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Keywords
Multi-entity Bayesian networks, First-Order Bayesian Logic, Probabilistic Reasoning