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Lookup NU author(s): Professor Chris Oates
This is the final published version of an article that has been published in its final definitive form by International Statistical Institute, 2019.
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© 2019 ISI/BS. Gradient information on the sampling distribution can be used to reduce the variance of Monte Carlo estimators via Stein’s method. An important application is that of estimating an expectation of a test function along the sample path of a Markov chain, where gradient information enables convergence rate improvement at the cost of a linear system which must be solved. The contribution of this paper is to establish theoretical bounds on convergence rates for a class of estimators based on Stein’s method. Our analysis accounts for (i) the degree of smoothness of the sampling distribution and test function, (ii) the dimension of the state space, and (iii) the case of non-independent samples arising from a Markov chain. These results provide insight into the rapid convergence of gradient-based estimators observed for low-dimensional problems, as well as clarifying a curse-of-dimension that appears inherent to such methods.
Author(s): Oates CJ, Cockayne J, Briol F-X, Girolami M
Publication type: Article
Publication status: Published
Online publication date: 06/03/2019
Acceptance date: 06/03/2019
Date deposited: 29/04/2019
ISSN (print): 1350-7265
ISSN (electronic): 1573-9759
Publisher: International Statistical Institute
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