Gaussian Processes indexed on the symmetric group: prediction and learning

Abstract : In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X. The Euclidean case is well known and has been widely studied. In this paper, we explore the less classical case where X is the non commutative finite group of permutations. In this setting, we propose and study an harmonic analysis of the covariance operators that enables to consider Gaussian processes models and forecasting issues. Our theory is motivated by statistical ranking problems.
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Pré-publication, Document de travail
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Contributeur : Baptiste Broto <>
Soumis le : dimanche 9 septembre 2018 - 19:32:03
Dernière modification le : jeudi 24 janvier 2019 - 13:45:49
Document(s) archivé(s) le : lundi 10 décembre 2018 - 12:21:17


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  • HAL Id : hal-01731251, version 3
  • ARXIV : 1803.06118


François Bachoc, Baptiste Broto, Fabrice Gamboa, Jean-Michel Loubes. Gaussian Processes indexed on the symmetric group: prediction and learning. 2018. 〈hal-01731251v3〉



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