A
Alan Genz
Researcher at Washington State University
Publications - 61
Citations - 5527
Alan Genz is an academic researcher from Washington State University. The author has contributed to research in topics: Numerical integration & Multivariate normal distribution. The author has an hindex of 27, co-authored 61 publications receiving 5180 citations. Previous affiliations of Alan Genz include University of Kent.
Papers
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Journal ArticleDOI
Numerical Computation of Multivariate Normal Probabilities
TL;DR: This article describes a transformation that simplifies the problem and places it into a form that allows efficient calculation using standard numerical multiple integration algorithms.
Book
Computation of Multivariate Normal and t Probabilities
Alan Genz,Frank Bretz +1 more
TL;DR: This book describes recently developed methods for accurate and efficient computation of the required probability values for problems with two or more variables.
Journal ArticleDOI
Comparison of Methods for the Computation of Multivariate t Probabilities
Alan Genz,Frank Bretz +1 more
TL;DR: In this article, the authors compared acceptance-rejection, spherical-radial transformations, and separation-of-variables transformations for hyper-rectangular integration regions, and showed that the most efficient numerical methods use a transformation developed by Genz for multivariate normal probabilities.
Methods for the Computation of Multivariate t-Probabilities ∗
TL;DR: In this article, the authors compared methods for the numerical computation of multivariate t-probabilities for hyperrectangular integration regions based on acceptance-rejection, spherical-radial transformations and separation-of-variables transformations, and showed that the most efficient numerical methods use a transformation developed by Genz (1992) for multivariate normal probabilities.
Journal ArticleDOI
An adaptive algorithm for the approximate calculation of multiple integrals
TL;DR: An adaptive algorithm for numerical integration over hyperrectangular regions is described that uses a globally adaptive subdivision strategy and has been structured to allow ecient implementation on shared memory parallel computers.