See: Description
Interface | Description |
---|---|
CongruentialSeeds |
Contains 10 seeds for use with the RandomNumber Congruential
that give non-overlapping streams.
|
ContinuousDistribution |
An interface for Classes that describe a continuous random variable distribution.
|
DiscreteDistribution |
An interface for Classes that describe discrete random variables.
|
DiscreteRandomVariate |
A RandomVariate that can only take on discrete values.
|
Distribution |
An interface for Classes that describe a random variable distribution.
|
LKSeeds |
Contains 100 seeds that will give non-overlapping streams of length 100,000
when used with the implementation of RandomNumer, Congruential.
|
Pooled |
A RandomNumber that is generated by combining the outputs of two
other RandomNumbers.
|
RandomNumber |
The common interface for all classes that generate random numbers.
|
RandomNumberStream | |
RandomObjectVariate<T> | |
RandomVariate |
The common interface for all random variate generation classes and the
parent interface for specialized random variate interfaces.
|
RandomVector |
The common interface for all random vector classes.
|
Class | Description |
---|---|
Antithetic |
Generates antithetic random numbers.
|
AR1Variate |
A simple first order auto regressive AR(1) process.
|
BernoulliDistribution |
A representation of the Bernoulli Distribution.
|
BernoulliVariate |
Generates Bernoulli random variates (either int or double).
Parameter: probability = P{X = 1} or the probability of "success"
in a single trial. |
BetaVariate |
Generates Beta(alpha, beta) random variates.
|
BinomialVariate |
Generates Binomial(n, p) random variates (representing the number of
successes in n iid Bernoulli(p) trials.
Parameters: n = number of Bernoulli trials
probability = P{X=1} for Bernoulli trials. |
BivariateNormalVector |
Generates bivariate normal vectors with given means, standard deviations, and
correlation.
|
CompositionVariate |
Generates the composition of a list of RandomVariates.
|
Congruential |
An implementation of a multiplicative Linear Congruential Generator.
|
ConstantIntegerVariate |
This always generates the same
(int) value . |
ConstantVariate |
A "RandomVariate" class that is constant.
|
ConvolutionVariate |
Generates the convolution (sum) of a number of RandomVariates.
|
CyclicalStepwiseIntegratedRate | |
DataVariate | |
DiscreteIntegerVariate |
Generates random variates having an arbitrary discrete distribution with
integer val.
|
DiscreteUniformVariate |
Generates Discrete Uniform (a, b) random variates.
Parameters: minimum = smallest possible value (integer).
maximum = largest possible value (integer). |
DiscreteVariate |
Generates random variates having an arbitrary discrete distribution.
|
Exponential_64Variate |
Generates Exponential random variate using log transform.
|
ExponentialTransform |
Generates a random variate that is the exponential transform of another
random variate.
|
ExponentialVariate |
Generates Exponential random variate using log transform.
|
FrequencyRandomObjectVariate | |
Gamma_64Variate |
Generates Gamma(alpha, beta) random variates.
|
GammaARVariate |
Instances of this class generate correlated Gamma random variates based on an
approach in Gaver and Lewis (1986).
|
GammaVariate |
Generates Gamma(alpha, beta) random variates.
|
Geometric_64Variate |
Generates random variates having a Geometric distribution.
|
GeometricVariate |
Generates random variates having a Geometric distribution.
|
IntegerTraceVariate | |
InverseGaussianVariate |
Generates random variates having the Inverse Gaussian distribution.
|
Log_64Transform |
Generates random variates that are the log transform of another random variate.
|
LogNormalVariate |
Generates random variates from the Log Normal(μ, σ) distribution
based on the exponential transformation of a Normal.
|
LogTransform |
Generates random variates that are the log transform of another random variate.
|
MarkovChainVariate |
Instances of this class generate values from a Markov Chain with the given
transition probabilities on the state space [0,..n-1] where n is the size of
the transition matrix.
|
MersenneTwister |
Mersenne Twister random number generator.
|
MersenneTwisterDC |
A clean port of the Mersenne Twister from the C code.
|
MersenneTwisterFactory |
Used to dynamically create instances of MersenneTwisterDC.
|
MersenneTwisterFactory.MaskNode | |
MersenneTwisterFactory.Polynomial | |
MersenneTwisterFactory.Vector | |
MixedVariate |
Generates random number from a mix of RandomVariates.
|
Mother |
The "Mother-of-All" random number generators.
|
MRG32k3a |
An ateempt to implement L'Ecuyer's MRG32k3a algorithm from
"Good Parameters and Implementations for Combined Multiple Recursive
Random Number Generators"
|
NegativeBinomialVariate |
Based on Devroye (1986), pp.
|
NHPoissonProcess_64Variate |
Generates interarrival times for a Non-homogenious (non-stationary) Poisson Process.
|
NHPoissonProcessVariate |
Generates interarrival times for a Non-homogenious (non-stationary) Poisson Process.
|
Normal02_64Variate |
Generates a Normal random variate.
|
Normal02Variate |
Generates a Normal random variate.
|
Normal03_64Variate |
Generates Normal random variates using Acceptance/Rejection
method.
|
Normal03Variate |
Generates Normal random variates using Acceptance/Rejection method.
|
Normal04Variate |
Generates Normal random variates using the Inverse Transform method
|
NormalVariate |
Generates Normal(μ, σ) random variate using the Box-Muller algorithm.
|
NormalVariate_64 |
Generates Normal(mean, std) random variate using the
Box-Muller algorithm.
|
NPPoissonProcessThinned_64Variate |
Generates a nonhomogeneous Poisson process variate using the method of
thinning by Lewis and Shedler.
|
NPPoissonProcessThinnedVariate |
Generates a nonhomogeneous Poisson process variate using the method of
thinning by Lewis and Shedler.
|
NSSrng |
An implementation of the RandomNumber Generator contained in the
Naval Simulation System.
|
OscillatingExponential_64Variate |
Generates an Exponential variate that is scaled by a sinusoid.
|
OscillatingExponentialVariate |
Generates an Exponential variate that is scaled by a sinusoid.
|
PearsonTypeVVariate |
Generates random variates having a Pearson Type V distribution.
|
Poisson2Variate | |
PoissonVariate |
Generates random variates having a Poisson distribution.
|
PooledGenerator | Deprecated
Use PooledXORGenerator instead
|
PooledGeneratorBase |
Base class for pooled generators.
|
PooledXORGenerator |
Pools two
RandomNumber instances by XOR-ing
their generated long values. |
RandomNumberFactory |
A factory for creating instances of
RandomNumber . |
RandomPointGenerator |
Generates a random Point2D as prescribed by a pair of RandomVariates.
|
RandomVariateBase |
The base class for Simkit's random variate generator classes.
|
RandomVariateFactory |
Factory for creating
RandomVariate instances from "orders". |
RandomVectorFactory |
Factory for creating
RandomVector instances from "orders". |
RenewalProcessVariate |
Generates arrival times for a renewal process.
|
ResampleVariate |
Generates random variates by resampling from a given data array.
|
RightWedgeVariate |
Generates a right wedge random variate.
|
RNG |
Generates Uniform(0,1) random numbers by
pooling the outcome of a Linear Congruential Generator (LCG) and a
Tausworthe generator.
|
RngStream |
Title: RngStream.java
Description: Multiple Streams and Substreams of Random Numbers Copyright: Pierre L'Ecuyer, University of Montreal Notice: This code can be used freely for personal, academic, or non-commercial purposes. |
RotatedBivariateNormalVector |
Generates bivariate normal random vectors whose correlation is determined by
the rotation rather than directly via ρ or covariance
σ12.
|
ScaledVariate |
Given a RandomVariate, this will scale and (optionally) shift it.
|
SequenceVariate |
Generates sequence of numbers starting with 0 and increasing by 1
|
Sequential |
Generates "random" numbers that are simply the last value + 1.
|
Tausworthe |
Implements a Tausworthe random number generator.
|
TraceVariate |
"Generates" numbers specified by the parameters.
|
TriangleVariate |
Generates Triangle random variates.
|
TruncatedNormalVariate |
Generates Normal variates truncated at 0.0.
|
TruncatedVariate |
Generates the maximum of a RandomVariate and a truncation point.
|
TwoStateMarkovVariate |
Generates values from a 2-D Markov Chain taking on values {0,1}.
|
UniformVariate |
Generates continuous uniform random variates.
|
Weibull_64Variate |
Generate from the Weibull distribution having pdf:
f(x) = αxα-1β-αe-(x/β)α, x > 0 |
WeibullVariate |
Generate from the Weibull distribution having pdf:
f(x) = αxα-1β-αe-(x/β)α, x > 0 |
A collection of Classes used to generate Random Numbers, Random Variates, and RandomVectors. The Classes are typically instantiated using one of the two factory Classes: RandomNumberFactory, RandomVariateFactory or RandomVectorFactory. The generation of random variates by the implementations of RandomVariate and RandomVector are supported by an instance of RandomNumber. Any of the implementations of RandomNumber can be specified as the supporter for the RandomVariates.
For classes using Math.log(), alternates using simkit.util.Math64.log() instead have been added. The 64-bit version is obtained by appending "_64" to the base name of the class. These classes are: