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.
Keywords for this software
References in zbMATH (referenced in 12 articles , 1 standard article )
Showing results 1 to 12 of 12.
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- Poole, David: Foundations of model construction in feature-based semantic science (2013)
- Wuillemin, Pierre-Henri; Torti, Lionel: Structured probabilistic inference (2012)
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- Santos, Eugene jun.; Wilkinson, John T.; Santos, Eunice E.: Fusing multiple Bayesian knowledge sources (2011)
- Laskey, Kathryn Blackmond; Wright, Edward J.; da Costa, Paulo C.G.: Envisioning uncertainty in geospatial information (2010)
- Howard, Catherine; Stumptner, Markus: Automated compilation of object-oriented probabilistic relational models (2009)
- Nedjah, Nadia (ed.); de Macedo Mourelle, Luiza (ed.); Kacprzyk, Janusz (ed.): Innovative applications in data mining (2009)
- Laskey, Kathryn Blackmond: MEBN: a language for first-order Bayesian knowledge bases (2008)
- Natarajan, Sriraam; Tadepalli, Prasad; Dietterich, Thomas G.; Fern, Alan: Learning first-order probabilistic models with combining rules (2008)
- Natarajan, Sriraam; Tadepalli, Prasad; Fern, Alan: A relational hierarchical model for decision-theoretic assistance (2008)