Modeling data envelopment analysis by chance method in hybrid uncertain environments
Mathematics and Computers in Simulation
Fast computation of high-dimensional multivariate normal probabilities
Computational Statistics & Data Analysis
Age-varying bivariate distribution models for growth prediction
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
A Gradient Formula for Linear Chance Constraints Under Gaussian Distribution
Mathematics of Operations Research
Computational Statistics & Data Analysis
Particle Algorithms for Optimization on Binary Spaces
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Level bundle methods for constrained convex optimization with various oracles
Computational Optimization and Applications
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Multivariate normal and t probabilities are needed for statistical inference in many applications. Modern statistical computation packages provide functions for the computation of these probabilities for problems with one or two variables. This book describes recently developed methods for accurate and efficient computation of the required probability values for problems with two or more variables. The book discusses methods for specialized problems as well as methods for general problems. The book includes examples that illustrate the probability computations for a variety of applications.