Abstract
Accurate computer methods are evaluated which transform uniformly distributed random numbers into quantities that follow gamma, beta, Poisson, binomial and negative-binomial distributions. All algorithms are designed for variable parameters. The known convenient methods are slow when the parameters are large. Therefore new procedures are introduced which can cope efficiently with parameters of all sizes. Some algorithms require sampling from the normal distribution as an intermediate step. In the reported computer experiments the normal deviates were obtained from a recent method which is also described.
Zusammenfassung
Zur Erzeugung nicht-gleichverteilter Zufallszahlen braucht man Methoden, die gleichverteilte Zufallszahlen in Größen der gegebenen Verteilung transformieren. Es werden Transformationen untersucht, die Gamma-, Beta-, Poisson- oder Binomial-verteilte Zufallszahlen produzieren. Approximative Verfahren werden nicht behandelt. Die bisher bekannten Algorithmen sind langsam, wenn die Parameter der Verteilungen groß sind. Daher werden neue Methoden eingeführt, die diesen Nachteil weitgehend vermeiden. In allen Verfahren dürfen die Parameter beliebig und jedesmal neu gewählt werden. Für manche Transformationen werden normalverteilte Zufallszahlen als Zwischenschritt benötigt; die hierfür verwendete Methode ist ebenfalls angegeben.
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This research was supported by the National Research Council of Canada and by Deutsche Forschungsgemeinschaft.
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Ahrens, J.H., Dieter, U. Computer methods for sampling from gamma, beta, poisson and bionomial distributions. Computing 12, 223–246 (1974). https://doi.org/10.1007/BF02293108
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DOI: https://doi.org/10.1007/BF02293108
Key words and phrases
- Random numbers
- pseudorandom
- normal distribution
- gamma distribution
- bei distribution
- Poisson distribution
- binomial distribution
- simulation
- numerical analysis