Approximate Query Answering in Complex Gaussian Mixture Models

Mattis Hartwig, Marcel Gehrke, Ralf Möller


Gaussian mixture models are widely used in a diverse range of research fields. If the number of components and dimensions grow high, the computational costs for answering queries become unreasonably high for practical use. Therefore approximation approaches are necessary to make complex Gaussian mixture models more usable. The need for approximation approaches is also driven by the relatively recent representations that theoretically allow unlimited number of mixture components (e.g. nonparametric Bayesian networks or infinite mixture models). In this paper we introduce an approximate inference algorithm that splits the existing algorithm for query answering into two steps and uses the knowledge from the first step to reduce unnecessary calculations in the second step while maintaining a defined error bound. In highly complex mixture models we observed significant time savings even with low error bounds.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Big Knowledge (ICBK)
Number of pages6
Publication date11.2019
Article number8944433
ISBN (Print)978-1-7281-4608-9
ISBN (Electronic)978-1-7281-4607-2
Publication statusPublished - 11.2019
Event10th IEEE International Conference on Big Knowledge - Beijing, China
Duration: 10.11.201911.11.2019
Conference number: 156494

Research Areas and Centers

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


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