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Lifted Dynamic Junction Tree Algorithm

  • Marcel Gehrke
  • Tanya Braun
  • Ralf Möller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10872)

Abstract

Probabilistic models involving relational and temporal aspects need exact and efficient inference algorithms. Existing approaches are approximative, include unnecessary grounding, or do not consider the relational and temporal aspects of the models. One approach for efficient reasoning on relational static models given multiple queries is the lifted junction tree algorithm. In addition, for propositional temporal models, the interface algorithm allows for efficient reasoning. To leverage the advantages of the two algorithms for relational temporal models, we present the lifted dynamic junction tree algorithm, an exact algorithm to answer multiple queries efficiently for probabilistic relational temporal models with known domains by reusing computations for multiple queries and multiple time steps. First experiments show computational savings while appropriately accounting for relational and temporal aspects of models.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Information SystemsUniversität zu LübeckLübeckGermany

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