The Beta Random Number Generating Function
Random generation for the beta distribution with parameters
rbeta(n, shape1, shape2)
Number of beta random numbers to generate. If
Positive shape parameters.
The beta distribution with parameters
shape1 = a and
shape2 = b
for a > 0, b > 0 and 0 ≤ x ≤ 1.
The mean is a/(a+b) and the variance is ab/((a+b)^2 (a+b+1)).
rbeta basically utilizes the following guideline primarily proposed by Hung
et al. (2009) for generating beta random numbers.
shape2) < 1, the B00 algorithm (Sakasegawa, 1983) is used;
shape1< 1 <
shape1> 1 >
shape2, the B01 algorithm (Sakasegawa, 1983) is used;
shape1) > 1, the B4PE algorithm (Schmeiser and Babu, 1980) is used if one papameter is close to 1 and the other is large (say > 4); otherwise, the BPRS algorithm (Zechner and Stadlober, 1993) is used.
rbeta generates beta random numbers.
Ching-Wei Cheng <email@example.com>,
Ying-Chao Hung <firstname.lastname@example.org>,
Narayanaswamy Balakrishnan <email@example.com>
rbeta uses a C translation of
Y. C. Hung and N. Balakrishnan and Y. T. Lin (2009), Evaluation of beta generation algorithms, Communications in Statistics - Simulation and Computation, 38:750–770.
Y. C. Hung and N. Balakrishnan and Y. T. Lin (2009), Evaluation of beta generation algorithms, Communications in Statistics - Simulation and Computation, 38, 750–770.
H. Sakasegawa (1983), Stratified rejection and squeeze method for generating beta random numbers, Annals of the Institute Statistical Mathematics, 35, 291–302.
B.W. Schmeiser and A.J.G. Babu (1980), Beta variate generation via exponential majorizing functions, Operations Research, 28, 917–926.
H. Zechner and E. Stadlober (1993), Generating beta variates via patchwork rejection, Computing, 50, 1–18.
rbeta in package stats.
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