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Simulating Bessel random variables

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  • Devroye, Luc

Abstract

In this paper, we discuss efficient exact random variate generation for the Bessel distribution. The expected time of the algorithm is uniformly bounded over all choices of the parameters, and the algorithm avoids any computation of Bessel functions or Bessel ratios.

Suggested Citation

  • Devroye, Luc, 2002. "Simulating Bessel random variables," Statistics & Probability Letters, Elsevier, vol. 57(3), pages 249-257, April.
    • Handle: RePEc:eee:stapro:v:57:y:2002:i:3:p:249-257
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    References listed on IDEAS

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    1. D. J. Best & N. I. Fisher, 1979. "Efficient Simulation of the von Mises Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(2), pages 152-157, June.
    2. Lin Yuan & John Kalbfleisch, 2000. "On the Bessel Distribution and Related Problems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 438-447, September.
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    Cited by:

    1. Roberto León-González, 2019. "Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 899-920, September.
    2. Akihiro Tanabe & Kenji Fukumizu & Shigeyuki Oba & Takashi Takenouchi & Shin Ishii, 2007. "Parameter estimation for von Mises–Fisher distributions," Computational Statistics, Springer, vol. 22(1), pages 145-157, April.
    3. Paul Glasserman & Kyoung-Kuk Kim, 2011. "Gamma expansion of the Heston stochastic volatility model," Finance and Stochastics, Springer, vol. 15(2), pages 267-296, June.
    4. T. Pellegrino & P. Sabino, 2015. "Enhancing Least Squares Monte Carlo with diffusion bridges: an application to energy facilities," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 761-772, May.
    5. 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.
    6. Sabelfeld Karl K., 2017. "Random walk on spheres algorithm for solving transient drift-diffusion-reaction problems," Monte Carlo Methods and Applications, De Gruyter, vol. 23(3), pages 189-212, September.
    7. Zhehan Jiang & Jonathan Templin, 2019. "Gibbs Samplers for Logistic Item Response Models via the Pólya–Gamma Distribution: A Computationally Efficient Data-Augmentation Strategy," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 358-374, June.
    8. Fotopoulos, Stergios B. & Jandhyala, Venkata K., 2004. "Bessel inequalities with applications to conditional log returns under GIG scale mixtures of normal vectors," Statistics & Probability Letters, Elsevier, vol. 66(2), pages 117-125, January.
    9. Wenbin Hu & Junzi Zhou, 2017. "Backward simulation methods for pricing American options under the CIR process," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1683-1695, November.

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