Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased
Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational …

Stein's method for measuring convergence to a continuous target distribution relies on an
operator characterizing the target and Stein factor bounds on the solutions of an associated …

We propose a new version of Stein's method of exchangeable pairs, which, given a suitable
exchangeable pair (W,W') of real-valued random variables, suggests the approximation of …

We provide non-asymptotic convergence rates of the Polyak-Ruppert averaged stochastic
gradient descent (SGD) to a normal random vector for a class of twice-differentiable test …

We establish general upper bounds on the Kolmogorov distance between two probability
distributions in terms of the distance between these distributions as measured with respect …

We extend Stein's celebrated Wasserstein bound for normal approximation via
exchangeable pairs to the multi-dimensional setting. As an intermediate step, we exploit the …

We introduce higher-order Stein kernels relative to the standard Gaussian measure, which
generalize the usual Stein kernels by involving higher-order derivatives of test functions. We …

We establish quantitative stability estimates for the Bakry-Émery bound on logarithmic
Sobolev and Poincaré constants of uniformly log-concave measures. More specifically, we …

We introduce a simple iterative technique for bounding derivatives of solutions of Stein
equations Lf=h-Eh(Z), where L is a linear differential operator and Z is the limit random …