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Poisson Random Variate Generation

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  • C. D. Kemp
  • Adrienne W. Kemp

Abstract

The paper examines the problem of generating Poisson random variates particularly when the parameter x may vary from call to call. A new algorithm based on a unidirectional search from the mode is proposed; the modal probability and modal cumulative probability, when required, are calculated by simple and rapid, yet extremely accurate, asymptotic approximations; a squeeze feature is incorporated. Timings for a Fortran 77 implementation show that the algorithm dominates the current state‐of‐the‐art algorithms for λ

Suggested Citation

  • C. D. Kemp & Adrienne W. Kemp, 1991. "Poisson Random Variate Generation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 143-158, March.
    • Handle: RePEc:bla:jorssc:v:40:y:1991:i:1:p:143-158
    DOI: 10.2307/2347913
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    Cited by:

    1. Makarov Roman N. & Glew Devin, 2010. "Exact simulation of Bessel diffusions," Monte Carlo Methods and Applications, De Gruyter, vol. 16(3-4), pages 283-306, January.
    2. Ian Iscoe & Asif Lakhany, 2011. "Adaptive Simulation of the Heston Model," Papers 1111.6067, arXiv.org.
    3. Ong, S.H. & Lee, Wen-Jau, 2008. "Computer generation of negative binomial variates by envelope rejection," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4175-4183, May.
    4. Kaeyoung Shin & Raghu Pasupathy, 2010. "An Algorithm for Fast Generation of Bivariate Poisson Random Vectors," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 81-92, February.

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