Nuclear physicists at the University of Washington developed a new framework to analyze how theoretical approximations ...

First-order derivatives: n additional function calls are needed. Second-order derivatives based on gradient calls, when the "grd" module is specified (Dennis and Schnabel 1983): n additional gradient ...

This paper presents a saddlepoint approximation to the cumulative distribution function of a random vector. The proposed approximation has accuracy comparable to that of existing expansions valid in ...

Particle methods are popular computational tools for Bayesian inference in nonlinear non-Gaussian state space models. For this class of models, we present two particle algorithms to compute the score ...

String theory began over 50 years ago as a way to understand the strong nuclear force. Since then, it’s grown to become a theory of everything, capable of explaining the nature of every particle, ...

The number represented by pi (π) is used in calculations whenever something round (or nearly so) is involved, such as for circles, spheres, cylinders, cones and ellipses. Its value is necessary to ...

Using photogrammetry, researchers created two facial approximations of an ancient Egyptian man. When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. A ...

The FD= and FDHESSIAN= options specify the use of finite difference approximations of the derivatives. The FD= option specifies that all derivatives are approximated using function evaluations, and ...