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 ...

Abstract: Existing approaches for low-rank approximation either need a rank prior or ignore the spatial smooth characteristic of a color image. To overcome these drawbacks, we propose a total ...

Abstract: Approximate computing is an effective low-power technique that improves circuits’ energy and latency performance by allowing acceptable output errors in the image processing design. This ...

Mathematics of Computation, Vol. 63, No. 207 (Jul., 1994), pp. 195-213 (19 pages) We deal with a method of enhanced convergence for the approximation of analytic functions. This method introduces ...

This article describes a generalized program for the computation of sampling errors. It employs computerized linearization of nonlinear estimates by the use of the first-order Taylor approximation. It ...

These videos, originally part of the Cochrane Learning Live webinar series, give an overview of the available methods for estimation of the between-study variance and its corresponding uncertainty, as ...

This is a short library that implements the complex-step derivative approximation algorithm for the computation of the N-derivative of an N-dimension function. This repository also includes the ...

Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many ...