Representation and Reasoning for Recursive Probability Models

  • Catherine Howard
  • Markus Stumptner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


This paper applies the Object Oriented Probabilistic Relational Modelling Language to recursive probability models. We present two novel anytime inference algorithms for recursive probability models expressed using this language. We discuss the strengths and limitations of these algorithms and compare their performance against the Iterative Structured Variable Elimination algorithm proposed for Probabilistic Relational Modelling Language using three different non-linear genetic recursive probability models.


Execution Time Model Construction Inference Algorithm Generic Instance Intelligence Model 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Catherine Howard
    • 1
  • Markus Stumptner
    • 2
  1. 1.Electronic Warfare and Radar DivisionDefence Science and Technology OrganisationEdinburghSouth Australia
  2. 2.Advanced Computing Research CenterUniversity of South AustraliaAdelaide

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