Monte Carlo variance reduction using Stein operators
Date: Friday, 21 May 2021
Time: 2-3 pm
Speaker: Dr. Leah South(Queensland University of Technology)
Abstract:
This talk will focus on two new methods for estimating posterior expectations when the derivatives of the log
posterior are available. The proposed methods are in a class of estimators that use Stein operators to generate
control variates or control functionals. The first method applies regularisation to improve the performance of
popular Stein-based control variates for high-dimensional Monte Carlo integration. The second method, referred
to as semi-exact control functionals (SECF), is based on control functionals and Sard’s approach to numerical
integration. The use of Sard’s approach ensures that our control functionals are exact on all polynomials
up to a fixed degree in the Bernstein-von-Mises limit. Several Bayesian inference examples will be used to
illustrate the potential for reduction in mean square error. If time permits, I will also briefly describe some
benefits and challenges of Stein-based control variates in the unbiased Markov chain Monte Carlo setting.
Zoom Link:
https://uow-au.zoom.us/j/85720215124?pwd=dDFzTm9zRDhrZExNK1FMTHNWSkY1dz09
Password: 274511