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Seminar Bayesian Inverse Problems

Dr. Claudia Schillings
Humboldt-Universität zu Berlin
Wintersemester 2016/17


The first meeting takes place on Tuesday, October 18th.

Time and Place

Short Description

Uncertainty quantification (UQ) is an interesting, fast growing research area aimed at developing methods to address the impact of parameter, data and model uncertainty in complex systems. In this seminar, we will focus on the identification of parameters through observations of the response of the system - the inverse problem. The uncertainty in the solution of the inverse problem will be described via the Bayesian approach. We will derive Bayes' theorem in the setting of finite dimensional parameter spaces, and discuss properties such as well-posedness, statistical estimates and connections to classical regularization methods. The remainder of this seminar will be devoted to algorithms for the efficient approximation of the solution of the Bayesian inverse problem. alternate text


  • J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems, Springer 2005.
  • K.J.H. Law, A.M. Stuart and K.C. Zygalakis, Data Assimilation: A Mathematical Introduction. Springer 2015.
  • S. Reich and C. Cotter, Probabilistic Forecasting and Bayesian Data Assimilation, Cambridge 2015.
  • Contact

    Dr. Claudia Schillings, claudia.schillings[at]


    Last update 18/10/2016