Stein's Method in Computational Statistics
24th April 2020, 2:00 pm – 2:45 pm
Fry Building, TBA
There is a recent trend in computational statistics to move away from sampling methods and towards optimisation methods for posterior approximation. These include discrepancy minimisation, gradient flows and control functionals - all of which have the potential to deliver faster convergence than a Monte Carlo method. In this talk we will provide a basic introduction to some of these algorithms, and then we will attempt to unify these emergent research themes in the context of Stein's method.
This seminar will be online (link is shared via mail-list)