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A Differential Approach for Staged Trees

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9161))

Abstract

Symbolic inference algorithms in Bayesian networks have now been applied in a variety of domains. These often require the computation of the derivatives of polynomials representing probabilities in such graphical models. In this paper we formalise a symbolic approach for staged trees, a model class making it possible to visualise asymmetric model constraints. We are able to show that the probability parametrisation associated to trees has several advantages over the one associated to Bayesian networks. We then continue to compute certain derivatives of staged trees’ polynomials and show their probabilistic interpretation. We are able to determine that these polynomials can be straightforwardly deduced by compiling a tree into an arithmetic circuit.

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Author information

Authors and Affiliations

  1. Department of Statistics, The University of Warwick, Coventry, CV4 7AL, UK

    Christiane Görgen, Manuele Leonelli & James Q. Smith

Authors
  1. Christiane Görgen
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  2. Manuele Leonelli
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  3. James Q. Smith
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Corresponding author

Correspondence to Manuele Leonelli .

Editor information

Editors and Affiliations

  1. Université de Technologie de Compiègne, Compiègne, France

    Sébastien Destercke

  2. Centre de Recherches de Royallieu, Université de Technologie de Compiègne, Compiègne, France

    Thierry Denoeux

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Görgen, C., Leonelli, M., Smith, J.Q. (2015). A Differential Approach for Staged Trees. In: Destercke, S., Denoeux, T. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2015. Lecture Notes in Computer Science(), vol 9161. Springer, Cham. https://doi.org/10.1007/978-3-319-20807-7_31

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  • DOI: https://doi.org/10.1007/978-3-319-20807-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20806-0

  • Online ISBN: 978-3-319-20807-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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