19227611
Seminar
SoSe 18: Seminar Uncertainty Quantification
Tim Sullivan
Comments
For Summer Semester 2018 the UQ Seminar will focus on theory and methods for forward and inverse quantification of uncertainty in spatial statistical problems. We will consider methods such as Gaussian process regression and Kriging, reproducing kernel Hilbert spaces, parametric and non-parametric inverse problems, and approximate inference techniques such as Markov chain Monte Carlo, filters, smoothers, and transport maps.
closeSuggested reading
- Bühlmann, Peter; van de Geer, Sara. Statistics for High-Dimensional Data. Methods, Theory and Applications. Springer Series in Statistics. Springer, Heidelberg, 2011. xviii+556 pp. ISBN: 978-3-642-20191-2
- Kaipio, Jari; Somersalo, Erkki. Statistical and Computational Inverse Problems. Applied Mathematical Sciences, 160. Springer-Verlag, New York, 2005. xvi+339 pp. ISBN: 0-387-22073-9
- Reich, Sebastian; Cotter, Colin. Probabilistic Forecasting and Bayesian Data Assimilation. Cambridge University Press, New York, 2015. x+297 pp. ISBN: 978-1-107-66391-6; 978-1-107-06939-8
- Smith, Ralph C. Uncertainty Quantification. Theory, implementation, and applications. Computational Science & Engineering, 12. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2014. xviii+382 pp. ISBN: 978-1-611973-21-1
- Sullivan, T. J. Introduction to Uncertainty Quantification. Texts in Applied Mathematics, 63. Springer, Cham, 2015. xii+342 pp. ISBN: 978-3-319-23394-9; 978-3-319-23395-6
14 Class schedule
Regular appointments
Wed, 2018-04-18 10:00 - 12:00
Wed, 2018-04-25 10:00 - 12:00
Wed, 2018-05-02 10:00 - 12:00
Wed, 2018-05-09 10:00 - 12:00
Wed, 2018-05-16 10:00 - 12:00
Wed, 2018-05-23 10:00 - 12:00
Wed, 2018-05-30 10:00 - 12:00
Wed, 2018-06-06 10:00 - 12:00
Wed, 2018-06-13 10:00 - 12:00
Wed, 2018-06-20 10:00 - 12:00
Wed, 2018-06-27 10:00 - 12:00
Wed, 2018-07-04 10:00 - 12:00
Wed, 2018-07-11 10:00 - 12:00
Wed, 2018-07-18 10:00 - 12:00