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Random variate generation for the generalized inverse Gaussian distribution

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Abstract

We provide a uniformly efficient and simple random variate generator for the entire parameter range of the generalized inverse Gaussian distribution. A general algorithm is provided as well that works for all densities that are proportional to a log-concave function φ, even if the normalization constant is not known. It requires only black box access to φ and its derivative.

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Acknowledgement

I would like to thank both referees.

Author information

Correspondence to Luc Devroye.

Additional information

Research sponsored by NSERC Grant A3456.

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Devroye, L. Random variate generation for the generalized inverse Gaussian distribution. Stat Comput 24, 239–246 (2014). https://doi.org/10.1007/s11222-012-9367-z

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Keywords

  • Random variate generation
  • Simulation
  • Monte Carlo method
  • Expected time analysis