atmosphere.random

Members

Classes

GeneralizedHyperbolicRNG
class GeneralizedHyperbolicRNG(T, UniformRNG = Random)

Class to generate random observations from a generalized hyperbolic distribution using normal variance-mean mixture of generalized inverse Gaussian distribution.

GeneralizedInverseGaussianRNG
class GeneralizedInverseGaussianRNG(T, UniformRNG = Random)

Class to generate random observations from a generalized inverse Gaussian distribution.

GeneralizedVarianceGammaRNG
class GeneralizedVarianceGammaRNG(T, UniformRNG = Random)

Class to generate random observations from a generalized variance-gamma distribution using normal variance-mean mixture of generalized gamma distribution.

HyperbolicAsymmetricTRNG
class HyperbolicAsymmetricTRNG(T, UniformRNG = Random)

Class to generate random observations from a hyperbolic asymmetric t-distribution using normal variance-mean mixture of inverse-gamma distribution.

NormalInverseGaussianRNG
class NormalInverseGaussianRNG(T, UniformRNG = Random)

Class to generate random observations from a normal inverse Gaussian distribution using normal variance-mean mixture of inverse Gaussian distribution.

NormalVarianceMeanMixtureRNG
class NormalVarianceMeanMixtureRNG(T, UniformRNG = Random)

Class to create normal variance-mean mixture random number generators. Assume U has mixing probability density, Y ~ N(0, 1). Class constructs RNG for Z = Y*U^(1/2)+beta*U.

ProperGeneralizedHyperbolicRNG
class ProperGeneralizedHyperbolicRNG(T, UniformRNG = Random)

Class to generate random observations from a proper generalized hyperbolic distribution using normal variance-mean mixture of proper generalized inverse Gaussian distribution.

VarianceGammaRNG
class VarianceGammaRNG(T, UniformRNG = Random)

Class to generate random observations from a variance-gamma distribution using normal variance-mean mixture of gamma distribution.

Functions

rChiSquare
T rChiSquare(T shape)
T rChiSquare(ref UniformRNG rng, T shape)

Function to generate random observation from a Chi-squared distribution.

rExponential
T rExponential()
T rExponential(ref UniformRNG rng)

Function to generate random observation from standard exponential distribution.

rGamma
T rGamma(T shape)
T rGamma(ref UniformRNG rng, T shape)

Function to generate random observation from a gamma distribution.

rGeneralizedGamma
T rGeneralizedGamma(T shape, T power)
T rGeneralizedGamma(ref UniformRNG rng, T shape, T power)

Function to generate random observation from a generalized gamma distribution.

rInverseGamma
T rInverseGamma(T shape)
T rInverseGamma(ref UniformRNG rng, T shape)

Function to generate random observation from a inverse-gamma distribution.

rInverseGaussian
T rInverseGaussian(T lambda, T mu)
T rInverseGaussian(ref UniformRNG rng, T lambda, T mu)

Function to generate random observation from a inverse Gaussian distribution.

rNormal
T rNormal()
T rNormal(ref UniformRNG rng)

Function to generate random observationb from standard normal distribution.

rStudentT
T rStudentT(T shape)
T rStudentT(ref UniformRNG rng, T shape)

Function to generate random observation from a Student's t-distribution.

rWeibull
T rWeibull(T shape)
T rWeibull(ref UniformRNG rng, T power)

Function to generate random observation from a Weibull distribution.

toDistributionRNG
DistributionRNG!F toDistributionRNG(Rng rng)

Interfaces

DistributionRNG
interface DistributionRNG(T)

Interface for infinity input range of random numbers.

Structs

GammaSRNG
struct GammaSRNG(T, UniformRNG = Random)

Class to generate random observations from a gamma distribution.

GeneralizedGammaSRNG
struct GeneralizedGammaSRNG(T, UniformRNG = Random)

Class to generate random observations from a generalized gamma distribution.

InverseGammaSRNG
struct InverseGammaSRNG(T, UniformRNG = Random)

Class to generate random observations from a inverse-gamma distribution.

InverseGaussianSRNG
struct InverseGaussianSRNG(T, UniformRNG = Random)

Class to generate random observations from a inverse Gaussian distribution.

ProperGeneralizedInverseGaussianSRNG
struct ProperGeneralizedInverseGaussianSRNG(T, UniformRNG = Random)

Class to generate random observations from a proper (chi > 0, psi > 0) generalized inverse Gaussian distribution. The algorithm is based on that given by Dagpunar (1989).

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