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SSJ V. 1.2.5. |
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Description
Class Summary | |
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BetaGen | This class implements random variate generators with the beta distribution with shape parameters α > 0 and β > 0, over the interval (a, b), where a < b. |
BetaRejectionLoglogisticGen | Implements Beta random variate generators using the rejection method with log-logistic envelopes. |
BetaStratifiedRejectionGen | This class implements Beta random variate generators using the stratified rejection/patchwork rejection method. |
BetaSymmetricalBestGen | This class implements symmetrical beta random variate generators using Devroye's one-liner method. |
BetaSymmetricalGen | This class implements random variate generators with the symmetrical beta distribution with shape parameters α = β, over the interval (0, 1). |
BetaSymmetricalPolarGen | This class implements symmetrical beta random variate generators using Ulrich's polar method. |
BinomialConvolutionGen | Implements binomial random variate generators using the convolution method. |
BinomialGen | This class implements random variate generators for the binomial distribution. |
CauchyGen | This class implements random variate generators for the Cauchy distribution. |
ChiGen | This class implements random variate generators for the chi distribution. |
ChiRatioOfUniformsGen | This class implements Chi random variate generators using the ratio of uniforms method with shift. |
ChiSquareGen | This class implements random variate generators with the chi square distribution with n > 0 degrees of freedom. |
ErlangConvolutionGen | This class implements Erlang random variate generators using the convolution method. |
ErlangGen | This class implements random variate generators for the Erlang distribution with parameters k > 0 and λ > 0. |
ExponentialGen | This class implements random variate generators for the exponential distribution. |
ExtremeValueGen | This class implements random variate generators for the Gumbel (or extreme value) distribution. |
FatigueLifeGen | This class implements random variate generators for the Fatigue Life distribution with location parameter μ, scale parameter β and shape parameter γ. |
FisherFGen | This class implements random variate generators for the Fisher F-distribution with n and m degrees of freedom, where n and m are positive integers. |
GammaAcceptanceRejectionGen | This class implements gamma random variate generators using a method that combines acceptance-rejection with acceptance-complement. |
GammaGen | This class implements random variate generators for the gamma distribution. |
GammaRejectionLoglogisticGen | This class implements gamma random variate generators using a rejection method with loglogistic envelopes,. |
GeometricGen | This class implements a random variate generator for the geometric distribution. |
HyperbolicSecantGen | This class implements random variate generators for the Hyperbolic Secant distribution with location parameter μ and scale parameter σ. |
HypergeometricGen | This class implements random variate generators for the hypergeometric distribution. |
InverseGaussianGen | This class implements random variate generators for the inverse Gaussian distribution with location parameter μ > 0 and scale parameter λ > 0. |
KernelDensityGen | This class implements random variate generators for distributions obtained via kernel density estimation methods from a set of n individual observations x1,..., xn. |
KernelDensityVarCorrectGen | This class is a variant of KernelDensityGen , but with
a rescaling of the empirical distribution so that the variance
of the density used to generate the random variates is equal
to the empirical variance,
as suggested by Silverman. |
LaplaceGen | This class implements methods for generating random variates from the Laplace distribution. |
LogarithmicGen | This class implements random variate generators for the (discrete) logarithmic distribution. |
LogisticGen | This class implements random variate generators for the logistic distribution. |
LoglogisticGen | This class implements random variate generators for the Log-Logistic distribution with shape parameter α > 0 and scale parameter β > 0. |
LognormalGen | This class implements methods for generating random variates from the lognormal distribution. |
LognormalSpecialGen | Implements methods for generating random variates from the lognormal distribution using an arbitrary normal random variate generator. |
NegativeBinomialGen | This class implements random variate generators having the negative binomial distribution. |
NormalACRGen | This class implements normal random variate generators using the acceptance-complement ratio method. |
NormalBoxMullerGen | This class implements normal random variate generators using the Box-Muller method. |
NormalGen | This class implements methods for generating random variates from the normal distribution N(μ, σ). |
NormalKindermannRamageGen | This class implements normal random variate generators using the Kindermann-Ramage method. |
NormalPolarGen | This class implements normal random variate generators using the polar method with rejection. |
ParetoGen | This class implements random variate generators for one of the Pareto distributions, with parameters α > 0 and β > 0. |
PascalConvolutionGen | Implements Pascal random variate generators by the convolution method. |
Pearson5Gen | This class implements random variate generators for the Pearson type V distribution with shape parameter α > 0 and scale parameter β > 0. |
Pearson6Gen | This class implements random variate generators for the Pearson type VI distribution with shape parameters α1 > 0 and α2 > 0, and scale parameter β > 0. |
PoissonGen | This class implements random variate generators having the Poisson distribution. |
PoissonTIACGen | This class implements random variate generators having the Poisson
distribution (see PoissonGen ). |
Rand1 | Deprecated. Use RandomVariateGen and RandomVariateGenInt instead. |
RandomVariateGen | This is the base class for all random variate generators over the real line. |
RandomVariateGenInt | This is the base class for all generators of discrete random variates over the set of integers. |
RandomVariateGenWithCache | This class represents a random variate generator whose values are cached for more efficiency when using common random numbers. |
StudentGen | This class implements methods for generating random variates from the Student distribution with n > 0 degrees of freedom. |
StudentPolarGen | This class implements Student random variate generators using the polar method of. |
TriangularGen | This class implements random variate generators for the triangular distribution. |
UniformGen | This class implements random variate generators for the (continuous) uniform distribution over the interval (a, b), where a and b are real numbers with a < b. |
UniformIntGen | This class implements a random variate generator for the uniform distribution over integers, over the interval [i, j]. |
UnuranContinuous | This class permits one to create continuous univariate distribution using UNURAN via its string API. |
UnuranDiscreteInt | This class permits one to create a discrete univariate distribution using UNURAN via its string API. |
UnuranEmpirical | This class permits one to create generators for empirical and quasi-empirical univariate distributions using UNURAN via its string interface. |
WeibullGen | This class implements random variate generators for the Weibull distribution. |
Exception Summary | |
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UnuranException | This type of unchecked exception is thrown when an error occurs inside the UNURAN package. |
This package provides a collection of classes for non-uniform random variate generation, primarily from standard distributions.
Each non-uniform random variate generator requires
at least one RandomStream
object
(from package rng
), used to
generate the underlying uniform random numbers.
With some variate generation methods (e.g., the rejection
method), the number of uniforms required to get a single non-uniform
variate varies from one call to the next.
In that case, an auxiliary stream is often used to preserve
the synchronization between random variates when implementing
variance-reduction methods.
The main random number stream is called a fixed number of times
per non-uniform variate generation. If more uniform random numbers
are needed, they are obtained from the auxiliary stream.
For these types of generators, two
RandomStream
objects
should be passed to the constructor.
Otherwise, by default, the same stream will be used for all uniforms.
The generic classes
RandomVariateGen
and
RandomVariateGenInt
permit one to construct a random variate generator from a random
stream and an arbitrary distribution
(from the package Distribution
).
To generate random variates by inversion from an arbitrary
distribution over the real numbers, using a given random stream,
one can construct a
RandomVariateGen
object
with the desired (previously created)
Distribution
and RandomStream
objects,
and then call its
nextDouble
method.
For discrete distributions over the integers, one can construct a
RandomVariateGenInt
object that contains the
desired DiscreteDistributionInt
and RandomStream
,
and call its
nextInt
method.
By default, these generators simply call the
inverseF
method from the specified distribution object.
To generate random variates by other methods than inversion,
one can use specialized classes that extend
RandomVariateGen
or RandomVariateGenInt
.
Such classes are provided for a variety of standard discrete and
continuous distributions.
For example, NormalGen
extends RandomVariateGen
and provides normal random variate generators based on inversion.
Subclasses of NormalGen
implement various non-inversion normal variate generation methods.
To generate random variates with a specific method, it suffices to
invoke the constructor of the appropriate subclass and then call its
nextDouble method.
In most cases, the specialized classes maintain local copies of the
distribution parameters and use them for variate generation.
If the parameters of the contained distribution objects are later modified,
this may lead to inconsistencies: the variate generator object will
keep using the old values.
In fact, the constructors of the specialized classes often precompute
constants and tables based on these parameter values, which would have
to be recomputed if the parameters are changed.
On the other hand, the generic classes
RandomVariateGen
and
RandomVariateGenInt
call directly the inverseF method of the contained
distribution object, so they will always use the new parameter values
whenever the parameters in the distribution object are changed.
Static methods in the specialized classes allow the generation
of random variates from specific distributions without constructing a
RandomVariateGen
object.
This package also provides an interface to the
UNURAN (Universal Non-Uniform RANdom number generators) package,
a rich library of C functions designed and
implemented by the ARVAG (Automatic Random VAriate Generation)
project group in Vienna.
This interface can be used to access distributions or
generation methods not available directly in SSJ.
To get a UNURAN generator, it suffices to instantiate one
of the UNURAN interface classes:
UnuranDiscreteInt
for discrete random variates,
UnuranContinuous
for continuous ones (in one dimension), and
UnuranEmpirical
for quasi-empirical distributions based on experimental data.
The type of distribution and its parameters are specified to
UNURAN via its String API (see the UNURAN documentation).
Only univariate distributions are supported because
the UNURAN String API does not support the multivariate ones yet.
In the UNURAN interface classes,
nextDouble
and
nextInt
can be invoked as usual to generate variates,
but these methods are slowed down significantly by the overhead
in the interactions between code on the native side and on the Java side.
When several random variates are needed, it is much more efficient to
generate them in a single call, via the methods
nextArrayOfDouble
and
nextArrayOfInt
.
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SSJ V. 1.2.5. |
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PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |