The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Computer Generation of Random Variables Using the Ratio of Uniform Deviates
ACM Transactions on Mathematical Software (TOMS)
Gamma variate generators with increased shape parameter range
Communications of the ACM
Computer generation of gamma random variables—II
Communications of the ACM
Sampling from the gamma distribution on a computer
Communications of the ACM
Computer methods for sampling from the exponential and normal distributions
Communications of the ACM
Discrete-event simulation
A survey of methods for sampling from the gamma distribution
WSC '78 Proceedings of the 10th conference on Winter simulation - Volume 1
Efficient table-free sampling methods for the exponential, Cauchy, and normal distributions
Communications of the ACM
A rejection technique for sampling from T-concave distributions
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
A rejection technique for sampling from log-concave multivariate distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The patchwork rejection technique for sampling from unimodal distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Computer Generation of Poisson Deviates from Modified Normal Distributions
ACM Transactions on Mathematical Software (TOMS)
Algorithm 599: sampling from Gamma and Poisson distributions
ACM Transactions on Mathematical Software (TOMS)
A simple method for generating gamma variables
ACM Transactions on Mathematical Software (TOMS)
Stochastic resonance in noisy threshold neurons
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Random variate generation for exponentially and polynomially tilted stable distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Generating random numbers from a distribution specified by its Laplace transform
Statistics and Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the Fourth Symposium on Information and Communication Technology
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A suitable square root transformation of a gamma random variable with mean a ≥ 1 yields a probability density close to the standard normal density. A modification of the rejection technique then begins by sampling from the normal distribution, being able to accept and transform the initial normal observation quickly at least 85 percent of the time (95 percent if a ≥ 4). When used with efficient subroutines for sampling from the normal and exponential distributions, the resulting accurate method is significantly faster than competing algorithms.