Stein's method has emerged as a powerful and versatile tool in probability theory for deriving error bounds in distributional approximations. Originally developed to ...

Stein's method has emerged as a critical framework in the study of distributional approximations, providing quantitative bounds between probability distributions through the formulation and solution ...

In this paper we present a rigorous error analysis for the Lagrange-Galerkin method applied to convection-dominated diffusion problems. We prove new error estimates ...

Let P be the transition matrix of a positive recurrent Markov chain on the integers with invariant probability vector πT, and let(n) P̃ be a stochastic matrix, formed by augmenting the entries of the ...