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Apprentissage et sélection de réseaux bayésiens dynamiques pour les processus online non stationnaires

Author

Listed:
  • Matthieu Hourbracq

    (DECISION - LIP6 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Pierre-Henri Wuillemin

    (DECISION - LIP6 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Christophe Gonzales

    (DECISION - LIP6 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Philippe Baumard

    (LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - CNAM - Conservatoire National des Arts et Métiers [CNAM] - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université, ESD R3C - Équipe Sécurité & Défense - Renseignement, Criminologie, Crises, Cybermenaces - CNAM - Conservatoire National des Arts et Métiers [CNAM] - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

Abstract

Dynamic Bayesian Networks (DBNs) provide a principled scheme for modeling and learning conditional dependencies from complex multivariate time-series data. However, in most cases, the underlying generative Markov model is assumed to be homogeneous, mea- ning that neither its topology nor its parameters evolve over time. Therefore, learning a DBN to model a non-stationary process under this assumption will amount to poor predictions capa- bilities. Thus we build a framework to identify, in a streamed manner, transition times between underlying models and a framework to learn them in real time, without assumptions about their evolution. We propose a model for the dynamic of the transitions between modes stemming from Hidden semi-Markov Models (HsMMs) and Graphical Duration Models (GDMs). We show the method performances on simulated datasets.

Suggested Citation

  • Matthieu Hourbracq & Pierre-Henri Wuillemin & Christophe Gonzales & Philippe Baumard, 2018. "Apprentissage et sélection de réseaux bayésiens dynamiques pour les processus online non stationnaires," Post-Print hal-03228681, HAL.
    • Handle: RePEc:hal:journl:hal-03228681
    Note: View the original document on HAL open archive server: https://cnam.hal.science/hal-03228681
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