public DistributionRNG!F toDistributionRNG(Rng, F = ReturnType!(Rng.front))(Rng rng)
Example
DistributionRNG!double rng = GammaSRNG!double(rndGen, 1, 3).toDistributionRNG;
public T rExponential(T = double)()
if(isFloatingPoint!T)
Function to generate random observation from standard exponential distribution.
public T rExponential(T = double, UniformRNG)(ref UniformRNG rng)
if(isFloatingPoint!T && isUniformRNG!UniformRNG)
Function to generate random observation from standard exponential distribution.
Example
auto x = rExponential() * 5;
public T rGamma(T = double, UniformRNG)(
ref UniformRNG rng,
T shape)
if(isFloatingPoint!T && isUniformRNG!UniformRNG)
Function to generate random observation from a gamma distribution.
Contracts
in
{
assert (shape.isNormal);
assert (shape > 0);
}
References "Computer Generation of Statistical Distributions" by Richard Saucier
Example
auto x = rGamma(2.0) * 5;
public T rGeneralizedGamma(T = double)(
T shape,
T power)
if(isFloatingPoint!T)
Function to generate random observation from a generalized gamma distribution.
public T rGeneralizedGamma(T = double, UniformRNG)(
ref UniformRNG rng,
T shape,
T power)
if(isFloatingPoint!T && isUniformRNG!UniformRNG)
Function to generate random observation from a generalized gamma distribution.
Contracts
in
{
assert (power.isFinite);
assert (shape.isNormal);
assert (shape > 0);
}
Example
auto x = rGeneralizedGamma(0.8, 3.0) * 5;
public T rInverseGamma(T = double)(T shape)
if(isFloatingPoint!T)
Function to generate random observation from a inverse-gamma distribution.
public T rInverseGamma(T = double, UniformRNG)(
ref UniformRNG rng,
T shape)
if(isFloatingPoint!T && isUniformRNG!UniformRNG)
Function to generate random observation from a inverse-gamma distribution.
Contracts
in
{
assert (shape.isNormal);
assert (shape > 0);
}
Example
auto x = rInverseGamma(2.0) * 5;
public T rInverseGaussian(T = double)(
T lambda,
T mu)
if(isFloatingPoint!T)
Function to generate random observation from a inverse Gaussian distribution.
References
Michael, John R.; Schucany, William R.; Haas, Roy W. (May 1976). "Generating Random Variates Using Transformations with Multiple Roots".
public T rInverseGaussian(T = double, UniformRNG)(
ref UniformRNG rng,
T lambda,
T mu)
if(isFloatingPoint!T && isUniformRNG!UniformRNG)
Function to generate random observation from a inverse Gaussian distribution.
Contracts
in
{
assert (lambda.isNormal);
assert (lambda > 0);
assert (mu.isNormal);
assert (mu > 0);
}
References
Michael, John R.; Schucany, William R.; Haas, Roy W. (May 1976). "Generating Random Variates Using Transformations with Multiple Roots".
Example
auto x = rInverseGaussian(2.0, 3.0) * 5;
public T rChiSquare(T = double)(T shape)
if(isFloatingPoint!T)
Function to generate random observation from a Chi-squared distribution.
public T rChiSquare(T = double, UniformRNG)(
ref UniformRNG rng,
T shape)
if(isFloatingPoint!T && isUniformRNG!UniformRNG)
Function to generate random observation from a Chi-squared distribution.
Contracts
in
{
assert (shape.isNormal);
assert (shape > 0);
}
Example
auto x = rChiSquare(2.0) * 5;
Functions
toDistributionRNG | ||
rNormal | Function to generate random observationb from standard normal distribution. | |
rNormal | Function to generate random observationb from standard normal distribution. | |
rExponential | Function to generate random observation from standard exponential distribution. | |
rExponential | Function to generate random observation from standard exponential distribution. | |
rGamma | Function to generate random observation from a gamma distribution. | |
rGamma | Function to generate random observation from a gamma distribution. | |
rGeneralizedGamma | Function to generate random observation from a generalized gamma distribution. | |
rGeneralizedGamma | Function to generate random observation from a generalized gamma distribution. | |
rInverseGamma | Function to generate random observation from a inverse-gamma distribution. | |
rInverseGamma | Function to generate random observation from a inverse-gamma distribution. | |
rInverseGaussian | Function to generate random observation from a inverse Gaussian distribution. | |
rInverseGaussian | Function to generate random observation from a inverse Gaussian distribution. | |
rChiSquare | Function to generate random observation from a Chi-squared distribution. | |
rChiSquare | Function to generate random observation from a Chi-squared distribution. | |
rStudentT | Function to generate random observation from a Student's t-distribution. | |
rStudentT | Function to generate random observation from a Student's t-distribution. | |
rWeibull | Function to generate random observation from a Weibull distribution. | |
rWeibull | Function to generate random observation from a Weibull distribution. |
Structs
GammaSRNG | Class to generate random observations from a gamma distribution. | |
InverseGammaSRNG | Class to generate random observations from a inverse-gamma distribution. | |
GeneralizedGammaSRNG | Class to generate random observations from a generalized gamma distribution. | |
InverseGaussianSRNG | Class to generate random observations from a inverse Gaussian distribution. | |
ProperGeneralizedInverseGaussianSRNG | 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). |
Interfaces
DistributionRNG | Interface for infinity input range of random numbers. |
Classes
NormalVarianceMeanMixtureRNG | Class to create normal variance-mean mixture random number generators.
Assume | |
GeneralizedInverseGaussianRNG | Class to generate random observations from a generalized inverse Gaussian distribution. | |
VarianceGammaRNG | Class to generate random observations from a variance-gamma distribution using normal variance-mean mixture of gamma distribution. | |
HyperbolicAsymmetricTRNG | Class to generate random observations from a hyperbolic asymmetric t-distribution using normal variance-mean mixture of inverse-gamma distribution. | |
GeneralizedVarianceGammaRNG | Class to generate random observations from a generalized variance-gamma distribution using normal variance-mean mixture of generalized gamma distribution. | |
NormalInverseGaussianRNG | Class to generate random observations from a normal inverse Gaussian distribution using normal variance-mean mixture of inverse Gaussian distribution. | |
ProperGeneralizedHyperbolicRNG | Class to generate random observations from a proper generalized hyperbolic distribution using normal variance-mean mixture of proper generalized inverse Gaussian distribution. | |
GeneralizedHyperbolicRNG | Class to generate random observations from a generalized hyperbolic distribution using normal variance-mean mixture of generalized inverse Gaussian distribution. |