Procs
proc randGaussian(mu = 0.0; sigma = 1.0; return_pari: bool): FloatPair {...}{.raises: [], tags: [].}
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Use Box-Muller method to generate a pair of Gaussian numbers.
proc randGaussian(mu = 0.0; sigma = 1.0): float {...}{.raises: [], tags: [].}
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Use Box-Muller method to generate a pair of Gaussian numbers.
Wiki: https://en.wikipedia.org/wiki/Box-Muller_transform
proc randGaussianTrunc(mu = 0.0; sigma = 1.0; lb = -2.0; ub = 2.0): float {...}{.raises: [], tags: [].}
proc randGaussianTanh(mu = 0.0; sigma = 1.0; lb = -2.0; ub = 2.0): float {...}{.raises: [], tags: [].}
proc randExponential(lambda = 1.0): float {...}{.raises: [], tags: [].}
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Use inverse CDF method to generate a Exponetial random numer.
Wiki: https://en.wikipedia.org/wiki/Exponential_distribution
Wiki: https://en.wikipedia.org/wiki/Inverse_transform_sampling
proc randLaplace(mu = 0.0; b = 1.0): float {...}{.raises: [], tags: [].}
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Use inverse CDF method to generate a Laplace random numer.
Wiki: https://en.wikipedia.org/wiki/Laplace_distribution
Wiki: https://en.wikipedia.org/wiki/Inverse_transform_sampling
proc randCauchy(x0 = 0.0; gamma = 1.0): float {...}{.raises: [], tags: [].}
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Use inverse CDF method to generate a Cauchy random numer.
Wiki: https://en.wikipedia.org/wiki/Cauchy_distribution
Wiki: https://en.wikipedia.org/wiki/Inverse_transform_sampling
proc randRayleigh(sigma = 1.0): float {...}{.raises: [], tags: [].}
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Use inverse CDF method to generate a Rayleigh random numer.
Wiki: https://en.wikipedia.org/wiki/Rayleigh_distribution
Wiki: https://en.wikipedia.org/wiki/Inverse_transform_sampling
proc randGamma(k = 1.0; theta = 2.0): float {...}{.raises: [], tags: [].}
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Wiki: https://en.wikipedia.org/wiki/Gamma_distribution
proc randLogNormal(mu = 0.0; sigma = 1.0): float {...}{.raises: [], tags: [].}
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Wiki: https://en.wikipedia.org/wiki/Log-normal_distribution
Method: first generate a Gaussian random number, and then take it to exp(.) function.
proc randChi2(df = 1): float {...}{.raises: [], tags: [].}
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Wiki: https://en.wikipedia.org/wiki/Chi-square_distribution
Method: The chi-square distribution (with d.o.f. df) is a special case of gamma distribution (Gamma(df/2, 2))
proc randF(df1 = 1; df2 = 1): float {...}{.raises: [], tags: [].}
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Wiki: https://en.wikipedia.org/wiki/F-distribution
Method: F-distribution is defined as ratio of two independent and scaled chi-squared random variables.
proc randStudent(df = 1): float {...}{.raises: [], tags: [].}
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Wiki: https://en.wikipedia.org/wiki/Student%27s_t-distribution
Method: Student t distribution is defined as ratio of standard normal random number devided by a Chi2 random numer (with d.o.f of df)
proc randBeta(alpha = 0.5; beta = 0.5): float {...}{.raises: [], tags: [].}
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Wiki: https://en.wikipedia.org/wiki/Beta_distribution
proc randLogistic(mu = 5.0; s = 2.0): float {...}{.raises: [], tags: [].}
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Use inverse CDF method to generate a Logistic random numer.
Wiki: https://en.wikipedia.org/wiki/Logistic_distribution
Wiki: https://en.wikipedia.org/wiki/Inverse_transform_sampling
proc randWeibull(lambda = 1.0; k = 0.5): float {...}{.raises: [], tags: [].}
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Use inverse CDF method to generate a Weibull random numer.
Wiki: https://en.wikipedia.org/wiki/Weibull_distribution
Wiki: https://en.wikipedia.org/wiki/Inverse_transform_sampling
proc randBinomial(p = 0.5; n = 1): int {...}{.raises: [], tags: [].}
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Use a truncated normal distribution to approximate the Binomial distribution.
Wiki: https://en.wikipedia.org/wiki/Binomial_distribution
proc randPoisson(lambda = 1.0): int {...}{.raises: [], tags: [].}
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Wiki: https://en.wikipedia.org/wiki/Poisson_distribution
