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Communication Dans Un Congrès Année : 2020

Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data

Pierre Clavier
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Olivier Bouaziz
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  • IdHAL : obouaziz
Gregory Nuel

Résumé

Sum-Product Networks (SPNs) can be seen as deep mixture models that have demonstrated efficient and tractable inference properties. In this context, graph and parameters learning have been deeply studied but the standard approaches do not apply to interval censored data. In this paper, we derive an approach for learning SPN parameters based on maximum likelihood using Expectation-Maximization (EM) in the context of interval censored data. Assuming the graph structure known, our algorithm makes possible to learn Gaussian leaves parameters of SPNs with right, left or interval censored data. We show that our EM algorithm for incomplete data outperforms other strategies such as the midpoint for censored intervals or dropping incomplete values.
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Dates et versions

hal-02859894 , version 1 (23-06-2020)

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  • HAL Id : hal-02859894 , version 1

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Pierre Clavier, Olivier Bouaziz, Gregory Nuel. Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data. Probabilistic and Graphical Models, Sep 2020, Aalborg, Denmark. ⟨hal-02859894⟩
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