A Comparison of Methods for Generating Normal Deviates on Digital Computers
Journal of the ACM (JACM)
A fast procedure for generating normal random variables
Communications of the ACM
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
A Gaussian Noise Generator for Hardware-Based Simulations
IEEE Transactions on Computers
Multivariate Gaussian Random Number Generation Targeting Reconfigurable Hardware
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Multivariate Gaussian Random Number Generator Targeting Specific Resource Utilization in an FPGA
ARC '08 Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications
Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization
Computational Optimization and Applications
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Three methods for generating outcomes on multivariate normal random vectors with a specified variance-covariance matrix are presented. A comparison is made to determine which method requires the least computer execution time and memory space when utilizing the IBM 360/67. All methods use as a basis a standard Gaussian random number generator. Results of the comparison indicate that the method based on triangular factorization of the covariance matrix generally requires less memory space and computer time than the other two methods.