Uncertain Evidence for Probabilistic Relational Models

Marcel Gehrke*, Tanya Braun, Ralf Möller

*Corresponding author for this work


Standard approaches for inference in probabilistic relational models include lifted variable elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a model, employing LVE as a subroutine in its steps. LVE and LJT can only handle certain evidence. However, most events are not certain. The purpose of this paper is twofold, (i) to adapt LVE, presenting LVE$$^{evi}$$, to handle uncertain evidence and (ii) to incorporate uncertain evidence for multiple queries in LJT, presenting LJT$$^{evi}$$. With LVE$$^{evi}$$ and LJT$$^{evi}$$, we can handle uncertain evidence for probabilistic relational models, while benefiting from the lifting idea. Further, we show that uncertain evidence does not have a detrimental effect on completeness results and leads to similar runtimes as certain evidence.

Original languageEnglish
Title of host publicationCanadian AI 2019: Advances in Artificial Intelligence
EditorsMarie-Jean Meurs, Frank Rudzicz
Number of pages14
Volume11489 LNAI
PublisherSpringer, Cham
Publication date24.04.2019
ISBN (Print)978-3-030-18304-2
ISBN (Electronic)978-3-030-18305-9
Publication statusPublished - 24.04.2019
Event32nd Canadian Conference on Artificial Intelligence - Kingston, Canada
Duration: 28.05.201931.05.2019

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems


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