Convergence rates for a class of estimators based on Stein’s method

By Chris J. Oates, Jon Cockayne, François‐Xavier Briol, Mark A. Girolami. Bernoulli, 2016

61 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/902c5ca2acbbe4c5e6139cd95278b0bd67b32338

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Citations in the corpus

Measuring Sample Quality with Kernels (2017, methodology): “On compact domains, where tightness is no longer an issue, the combined results of (Oates et al., 2016a, Lem.”