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.
The 2015 Q4 issue of SIAM Review will carry an article by Houman Owhadi, Clint Scovel, and myself on the brittle dependency of Bayesian posteriors as a function of the prior. This is an abbreviated presentation of results given in full earlier this year in Elec. J. Stat. The PDF is available for free under the terms of the Creative Commons 4.0 licence.
H. Owhadi, C. Scovel & T. J. Sullivan. “On the Brittleness of Bayesian Inference” SIAM Review 57(4):566–582, 2015. doi:10.1137/130938633
The Electronic Journal of Statistics has published an article by Houman Owhadi, Clint Scovel, and myself on the brittle dependency of Bayesian posteriors as a function of the prior.
H. Owhadi, C. Scovel & T. J. Sullivan. “Brittleness of Bayesian inference under finite information in a continuous world” Electronic Journal of Statistics 9:1–79, 2015. doi:10.1214/15-EJS989
Published on Tuesday 3 February 2015 at 10:00 UTC #publication