Journal ArticleDOI
Sampling from binomial and poisson distributions: A method with bounded computation times
Joachim H. Ahrens,Ulrich Dieter +1 more
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An accurate acceptance-rejection algorithm is devised and tested, which requires an average of less than 3 uniform deviates whenever the standard deviation σ of the distribution is at least 4, and this number decreases monotonically to 2.63 as σ→∞.Abstract:
An accurate acceptance-rejection algorithm is devised and tested. The procedure requires an average of less than 3 uniform deviates whenever the standard deviation σ of the distribution is at least 4, and this number decreases monotonically to 2.63 as σ→∞. Variable parameters are permitted, and no subroutines for sampling from other statistical distributions are needed.read more
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Book
Non-uniform random variate generation
TL;DR: A survey of the main methods in non-uniform random variate generation can be found in this article, where the authors provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes and Markov chain methods.
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VLASIC: A Catastrophic Fault Yield Simulator for Integrated Circuits
TL;DR: The yield simulator VLASIC (VLSI Layout Simulation for Integrated Circuits) is a Monte Carlo simulator that uses defect models and statistics to place random catastrophic point defects on a chip layout and determine what circuit faults, if any, have occurred.
Posted Content
Monte Carlo simulation and numerical integration
TL;DR: A survey of simulation methods in economics, with a specific focus on integration problems, is presented in this paper, where acceptance methods, importance sampling procedures, and Markov chain Monte Carlo methods for simulation from univariate and multivariate distributions and their application to the approximation of integrals.
Journal ArticleDOI
Binomial random variate generation
TL;DR: The new algorithm, BTPE, has fixed memory requirements and is faster than other such algorithms, both when single, or when many variates are needed.
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Computer Generation of Poisson Deviates from Modified Normal Distributions
Joachim H. Ahrens,Ulrich Dieter +1 more
TL;DR: Using efficient subprograms for generating uniform, exponential, alid normal deviates, the new algorithm is much faster than all competing methods.
References
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Handbook of Mathematical Functions
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The Art of Computer Programming
TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
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An Efficient Method for Generating Discrete Random Variables with General Distributions
TL;DR: The fast generation of discrete random variables with arbitrary frequency distributions is discussed, related to rejection techniques but differs from them in that all samples comprising the input data contribute to the samples in the target distribution.
Related Papers (5)
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