Abstract: With the ever decreasing transistor feature size in recent years, process variation has become a serious challenge in digital system design, mainly due to the undesirable timing uncertainty ...

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 ...

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 ...

Spatial variation is often encountered when large scale field trials are conducted which can result in biased estimation or prediction of treatment (i.e. genotype) values. An effective removal of ...

Notifications You must be signed in to change notification settings This assignment demonstrates data approximation using two methods: interpolation and the least squares method. The objective is to ...

This project implements the Heston Model for option pricing, utilizing the Fourier-Cosine (COS) method and the Almost Exact Simulation (AES) method. It includes Monte Carlo simulations, characteristic ...

The SURVEYMEANS and SURVEYREG procedures perform statistical analysis for survey data. These analytical procedures take into account the design used to select the sample. The sample design can be a ...