Uses of Interface
dev.nm.stat.random.rng.univariate.gamma.RandomGammaGenerator
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Packages that use RandomGammaGenerator Package Description dev.nm.stat.random.rng.univariate.beta dev.nm.stat.random.rng.univariate.gamma dev.nm.stat.random.rng.univariate.normal.truncated -
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Uses of RandomGammaGenerator in dev.nm.stat.random.rng.univariate.beta
Constructors in dev.nm.stat.random.rng.univariate.beta with parameters of type RandomGammaGenerator Constructor Description VanDerWaerden1969(RandomGammaGenerator X, RandomGammaGenerator Y)
Deprecated.Construct a random number generator to sample from the beta distribution. -
Uses of RandomGammaGenerator in dev.nm.stat.random.rng.univariate.gamma
Classes in dev.nm.stat.random.rng.univariate.gamma that implement RandomGammaGenerator Modifier and Type Class Description class
InverseTransformSamplingGammaRNG
Deprecated.There exist much more efficient algorithms.class
KunduGupta2007
Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution, when the shape parameter lies between 0 and 1.class
MarsagliaTsang2000
Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate.class
XiTanLiu2010a
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.class
XiTanLiu2010b
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable. -
Uses of RandomGammaGenerator in dev.nm.stat.random.rng.univariate.normal.truncated
Classes in dev.nm.stat.random.rng.univariate.normal.truncated that implement RandomGammaGenerator Modifier and Type Class Description class
InverseTransformSamplingTruncatedNormalRNG
A random variate x defined as \[ x = \Phi^{-1}( \Phi(\alpha) + U\cdot(\Phi(\beta)-\Phi(\alpha)))\sigma + \mu \] with \(\Phi\) the cumulative distribution function and \(\Phi^{-1}\) its inverse, U a uniform random number on (0, 1), follows the distribution truncated to the range (a, b).
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