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Incremental Junction Tree Inference

  • Hamza Agli
  • Philippe Bonnard
  • Christophe GonzalesEmail author
  • Pierre-Henri Wuillemin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 610)

Abstract

Performing probabilistic inference in multi-target dynamic systems is a challenging task. When the system, its evidence and/or its targets evolve, most of the inference algorithms either recompute everything from scratch, even though incremental changes do not invalidate all the previous computations, or do not fully exploit incrementality to minimize computations. This incurs strong unnecessary overheads when the system under study is large. To alleviate this problem, we propose in this paper a new junction tree-based message-passing inference algorithm that, given a new query, minimizes computations by identifying precisely the set of messages that differ from the preceding computations. Experimental results highlight the efficiency of our approach.

Keywords

Bayesian networks Incremental inference Junction tree 

Notes

Acknowledgments

This work was partially supported by IBM France Lab/ANRT CIFRE grant #2014/421.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hamza Agli
    • 1
  • Philippe Bonnard
    • 1
  • Christophe Gonzales
    • 2
    Email author
  • Pierre-Henri Wuillemin
    • 2
  1. 1.IBM France LabGentillyFrance
  2. 2.Sorbonne Universités, UPMC Univ Paris 6, CNRS, UMR 7606 LIP6ParisFrance

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