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    A06 - Approximative Bayesian inference and model selection for stochastic differential equations (SDEs)

    Principal investigators 

    Manfred Opper, TU Berlin

    Sebastian Reich, University of Potsdam, Department of Mathematics

    Vladimir Spokoiny, Weierstrass-Institute Berlin (WIAS)

     

    Funding Period

    01.07.2017 – 30.06.2021

     

    Open Positions

    Currently none available 

    Project Description

    This project is concerned with Bayesian semi-parametric and fully nonparametric approaches for estimating drift functions in systems of stochastic differential equations (SDEs). We will develop robust and computational efficient sequential Monte Carlo approaches and variational Bayesian methods and will study their convergence rates and approximation properties. We will also derive new methods for Bayesian model selection to decide on the base of available data which prior from a given collection is most appropriate for the SDE estimation problem at hand.

    The project will be carried out jointly between the

    1. TU Berlin, where the focus will be on variational Bayesian methods on combined state and drift estimation for SDEs,
    2. the Weierstrass-Institut Berlin, where the focus will be on prior selection for semi- and non-parametric statistics applied to SDEs, and
    3. the University of Potsdam, where the focus will be on sequential Monte Carlo methods for high-dimensional inference problems arising from SDEs.

    The project will closely collaborate with project A01 (statistics of SPDEs), project A02 (stability and accuracy of particle filters) and several of the more applied projects from Research Area B.

    Principal investigators 

    Manfred Opper, TU Berlin

    Sebastian Reich, University of Potsdam, Department of Mathematics

    Vladimir Spokoiny, Weierstrass-Institute Berlin (WIAS)

     

    Funding Period

    01.07.2017 – 30.06.2021

     

    Open Positions

    Currently none available 


    References: