Anirban Mukhopadhyay will give a short course on Machine Learning in Image Analysis on 29–31 March 2016 at the Zuse Institute Berlin.
Time and Place. 10:00–12:00 on 29, 30, and 31 March 2016, ZIB Lecture Hall.
For further information, see the course web-page at www.zib.de/MLIA.
Published on Tuesday 22 March 2016 at 10:00 UTC #event
From 12–16 September 2016, there will be a summer school on uncertainty quantification jointly organized by the GAMM Activity Group on Uncertainty Quantification, the International Research and Training Group Munich-Graz and WIAS. The invited lecturers are:
- Howard Elman (U Maryland): Reduced Basis Methods for Random PDEs
- Lars Grasedyck (RWTH Aachen): Sparse Tensor Approximations
- Claudia Schillings (U Warwick): Ensemble Kalman Filters and MCMC for Bayesian Inversion
- Raul Tempone (KAUST): Multilevel and Multiindex Monte-Carlo Methods
- Bruno Sudret (ETH Zürich): Engineering Risk Analysis with UQ-LAB
Contact the organisers at firstname.lastname@example.org for further information.
Published on Thursday 17 March 2016 at 16:00 UTC #event
Ingmar Schuster (Université Paris-Dauphine) “Gradient Importance Sampling”
Time and Place. Friday 11 March 2016, 11:15–12:45, Room 126 of Arnimallee 6 (Pi-Gebäude), 14195 Berlin
Abstract. Adaptive Monte Carlo schemes developed over the last years usually seek to ensure ergodicity of the sampling process in line with MCMC tradition. This poses constraints on what is possible in terms of adaptation. In the general case ergodicity can only be guaranteed if adaptation is diminished at a certain rate. Importance Sampling approaches offer a way to circumvent this limitation and design sampling algorithms that keep adapting. Here I present an adaptive variant of the discretized Langevin algorithm for estimating integrals with respect to some target density that uses an Importance Sampling instead of the usual Metropolis–Hastings correction.
A 350-page introduction to the key mathematical ideas underlying uncertainty quantification, designed as a course text or self-study for finalist undergraduates, master\'s students, or beginning doctoral students.
T. J. Sullivan. Introduction to Uncertainty Quantification, volume 63 of Texts in Applied Mathematics. Springer, 2015. ISBN 978-3-319-23394-9 (hardcover) 978-3-319-23395-6 (e-book) doi:10.1007/978-3-319-23395-6
Update, 11 March 2016. A list of errata can now be found here.
There is an opening in my research group for a postdoctoral researcher in Uncertainty Quantification. Strong candidates with backgrounds in mathematics, statistics, or computational science are encouraged to apply. For details see:
Review of applications will begin on 11 January 2016 and will continue until the post is filled.