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.

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References in zbMATH (referenced in 15 articles , 1 standard article )

Showing results 1 to 15 of 15.
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  1. Cozman, Fabio G.; Mauá, Denis D.: The complexity of Bayesian networks specified by propositional and relational languages (2018)
  2. Carvalho, Rommel N.; Laskey, Kathryn B.; Costa, Paulo C. G.: PR-OWL - a language for defining probabilistic ontologies (2017)
  3. Michels, Steffen; Hommersom, Arjen; Lucas, Peter J. F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)
  4. Grześ, Marek; Hoey, Jesse; Khan, Shehroz S.; Mihailidis, Alex; Czarnuch, Stephen; Jackson, Dan; Monk, Andrew: Relational approach to knowledge engineering for POMDP-based assistance systems as a translation of a psychological model (2014) ioport
  5. Howard, Catherine; Stumptner, Markus: A survey of directed entity-relation-based first-order probabilistic languages (2014)
  6. Poole, David: Foundations of model construction in feature-based semantic science (2013) ioport
  7. Wuillemin, Pierre-Henri; Torti, Lionel: Structured probabilistic inference (2012)
  8. Gonzales, Christophe; Wuillemin, Pierre-Henri: PRM inference using Jaffray & Faÿ’s local conditioning (2011)
  9. Santos, Eugene jun.; Wilkinson, John T.; Santos, Eunice E.: Fusing multiple Bayesian knowledge sources (2011) ioport
  10. Laskey, Kathryn Blackmond; Wright, Edward J.; da Costa, Paulo C. G.: Envisioning uncertainty in geospatial information (2010) ioport
  11. Howard, Catherine; Stumptner, Markus: Automated compilation of object-oriented probabilistic relational models (2009)
  12. Nedjah, Nadia (ed.); de Macedo Mourelle, Luiza (ed.); Kacprzyk, Janusz (ed.): Innovative applications in data mining (2009)
  13. Laskey, Kathryn Blackmond: MEBN: a language for first-order Bayesian knowledge bases (2008)
  14. Natarajan, Sriraam; Tadepalli, Prasad; Dietterich, Thomas G.; Fern, Alan: Learning first-order probabilistic models with combining rules (2008)
  15. Natarajan, Sriraam; Tadepalli, Prasad; Fern, Alan: A relational hierarchical model for decision-theoretic assistance (2008)