Unbiased estimators and multilevel Monte Carlo

Matti Vihola (JYU)

May 05. 2020, 14:45 -- 15:25

Multilevel Monte Carlo (MLMC) and recently proposed debiasing schemes are closely related methods which can be applied in scenarios where exact simulation methods are difficult to implement, but biased estimators are easily available. Unbiasedness is important when multiple estimators are combined. This talk is about a general class of unbiased estimators which admits earlier debiasing schemes as special cases, and which accomodates new lower variance estimators which behave asymptotically like MLMC, both in terms of variance and cost, under general conditions. This suggests that bias can often be eliminated entirely with arbitrarily small extra cost.

(The talk is based on: Oper. Res. 2018;  66(2):448-462.)


Further Information
Erwin Schrödinger Institute - virtual
Associated Event:
Multilevel and multifidelity sampling methods in UQ for PDEs (Online Workshop)
Kody Law (U Manchester)
Fabio Nobile (EPFL Lausanne)
Robert Scheichl (U Heidelberg)
Karen Willcox (U of Texas, Austin)