STAZHYNSKI Uladzislau

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Affiliations
  • 2016 - 2019
    Centre de mathématiques appliquées
  • 2017 - 2018
    Communauté d'universités et établissements Université Paris-Saclay
  • 2017 - 2018
    Ecole doctorale de mathematiques hadamard (edmh)
  • 2017 - 2018
    Ecole Polytechnique
  • 2016 - 2017
    Département de mathématiques et applications de l'ENS
  • 2019
  • 2018
  • 2017
  • Quantification of uncertainty for Stochastic Approximation.

    Stephane CREPEY, Gersende FORT, Emmanuel GOBET, Uladzislau STAZHYNSKI
    Actes de Conférence du Colloque GRETSI 2019 | 2019
    Stochastic Approximation is an iterative procedure for computing a zero θ of a function that is not explicit but defined as an expectation. It is, for example, a numerical tool for computing maximum likelihood in "regular" latent data models. If the definition of the statistical model is tainted with an uncertainty τ , of which only an a priori dπ(τ ) is known, then the zeros depend on τ and the natural question is to explore their distribution when τ ∼ dπ. In this paper, we propose an iterative algorithm based on a Stochastic Approximation scheme that,in the limit, computes θ (τ) for any τ and produces a characterization of its distribution. and weenounce sufficient conditions for the convergence of this algorithm.
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