Handling Overlaps When Lifting Gaussian Bayesian Networks.

Mattis Hartwig, Tanya Braun, Ralf Möller


Gaussian Bayesian networks are widely used for
modeling the behavior of continuous random variables. Lifting exploits symmetries when dealing with large numbers of isomorphic random variables. It provides a more compact representation for more efficient query answering by encoding the symmetries using logical variables. This paper improves on an existing lifted representation of the joint distribution represented by a Gaussian Bayesian network (lifted joint), allowing overlaps between the logical variables. Handling overlaps without grounding a model is critical for modelling real-world scenarios. Specifically, this paper contributes (i) a lifted joint that allows overlaps in logical variables and (ii) a lifted query answering algorithm using the lifted joint. Complexity analyses and experimental results show that — despite overlaps — constructing a lifted joint and answering queries on the lifted joint outperform their grounded counterparts significantly.
Original languageEnglish
Title of host publicationIJCAI
Number of pages7
Publication date2021
Publication statusPublished - 2021

Research Areas and Centers

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


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