Handling Overlaps When Lifting Gaussian Bayesian Networks.

Mattis Hartwig, Tanya Braun, Ralf Möller

Abstract

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.
OriginalspracheEnglisch
TitelIJCAI
Seitenumfang7
Erscheinungsdatum2021
Seiten4228-4234
DOIs
PublikationsstatusVeröffentlicht - 2021

Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Querschnittsbereich: Intelligente Systeme

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