Uses of Interface
dev.nm.stat.random.rng.univariate.RandomNumberGenerator
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Uses of RandomNumberGenerator in dev.nm.algebra.linear.matrix.doubles.operation
Methods in dev.nm.algebra.linear.matrix.doubles.operation with parameters of type RandomNumberGenerator Modifier and Type Method Description static DenseMatrix
MatrixFactory. randomDenseMatrix(int nRows, int nCols, RandomNumberGenerator rng)
Constructs a random DenseMatrix.static LowerTriangularMatrix
MatrixFactory. randomLowerTriangularMatrix(int dim, RandomNumberGenerator rng)
Constructs a random LowerTriangularMatrix.static Matrix
MatrixFactory. randomPositiveDefiniteMatrix(int dim, RandomNumberGenerator rng)
Constructs a random symmetric, positive definite matrix.static SymmetricMatrix
MatrixFactory. randomSymmetricMatrix(int dim, RandomNumberGenerator rng)
Constructs a random SymmetricMatrix.static UpperTriangularMatrix
MatrixFactory. randomUpperTriangularMatrix(int dim, RandomNumberGenerator rng)
Constructs a random UpperTriangularMatrix. -
Uses of RandomNumberGenerator in dev.nm.stat.evt.evd.univariate.rng
Classes in dev.nm.stat.evt.evd.univariate.rng that implement RandomNumberGenerator Modifier and Type Class Description class
InverseTransformSamplingEVDRNG
Generate random numbers according to a given univariate extreme value distribution, by inverse transform sampling. -
Uses of RandomNumberGenerator in dev.nm.stat.evt.markovchain
Classes in dev.nm.stat.evt.markovchain that implement RandomNumberGenerator Modifier and Type Class Description class
ExtremeValueMC
Simulation of first order extreme value Markov chains such that each pair of consecutive values has the dependence structure of given bivariate extreme value distributions.Constructors in dev.nm.stat.evt.markovchain with parameters of type RandomNumberGenerator Constructor Description ExtremeValueMC(BivariateEVD bivariate, ExtremeValueMC.MarginalDistributionType marginalType, RandomNumberGenerator uniformRng)
Create an instance with a given bivariate distribution that defines the dependence structure between two consecutive simulated values, and a uniform random number generator. -
Uses of RandomNumberGenerator in dev.nm.stat.evt.timeseries
Classes in dev.nm.stat.evt.timeseries that implement RandomNumberGenerator Modifier and Type Class Description class
MARMASim
Generate random numbers based on a given MARMA model.Constructors in dev.nm.stat.evt.timeseries with parameters of type RandomNumberGenerator Constructor Description MARMASim(MARMAModel model, RandomNumberGenerator generator)
Create an instance with the givenMARMAModel
, but override the innovation generation by the the given generator.MARMASim(MARMAModel model, RandomNumberGenerator generator, double[] initials)
Create an instance with the givenMARMAModel
and initial values, but override the innovation generation by the the given generator. -
Uses of RandomNumberGenerator in dev.nm.stat.hmm
Classes in dev.nm.stat.hmm that implement RandomNumberGenerator Modifier and Type Class Description class
HiddenMarkovModel
class
HMMRNG
In a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible.Constructors in dev.nm.stat.hmm with parameters of type RandomNumberGenerator Constructor Description HiddenMarkovModel(Vector PI, Matrix A, RandomNumberGenerator[] B)
HMMRNG(Vector PI, Matrix A, RandomNumberGenerator[] B)
Constructs a hidden Markov model. -
Uses of RandomNumberGenerator in dev.nm.stat.hmm.discrete
Classes in dev.nm.stat.hmm.discrete that implement RandomNumberGenerator Modifier and Type Class Description class
BaumWelch
This implementation trains an HMM model by observations using the Baum–Welch algorithm.class
DiscreteHMM
This is the discrete hidden Markov model as defined by Rabiner. -
Uses of RandomNumberGenerator in dev.nm.stat.hmm.mixture
Classes in dev.nm.stat.hmm.mixture that implement RandomNumberGenerator Modifier and Type Class Description class
MixtureHMM
This is the mixture hidden Markov model (HMM).class
MixtureHMMEM
The EM algorithm is used to find the unknown parameters of a hidden Markov model (HMM) by making use of the forward-backward algorithm. -
Uses of RandomNumberGenerator in dev.nm.stat.hmm.mixture.distribution
Methods in dev.nm.stat.hmm.mixture.distribution that return RandomNumberGenerator Modifier and Type Method Description RandomNumberGenerator[]
BetaMixtureDistribution. newRandomNumberGenerators()
RandomNumberGenerator[]
BinomialMixtureDistribution. newRandomNumberGenerators()
RandomNumberGenerator[]
ExponentialMixtureDistribution. newRandomNumberGenerators()
RandomNumberGenerator[]
GammaMixtureDistribution. newRandomNumberGenerators()
RandomNumberGenerator[]
LogNormalMixtureDistribution. newRandomNumberGenerators()
RandomNumberGenerator[]
MixtureDistribution. newRandomNumberGenerators()
Get the random number generators corresponding to the distributions (possibly differently parameterized) for all states.RandomNumberGenerator[]
NormalMixtureDistribution. newRandomNumberGenerators()
RandomNumberGenerator[]
PoissonMixtureDistribution. newRandomNumberGenerators()
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Uses of RandomNumberGenerator in dev.nm.stat.markovchain
Classes in dev.nm.stat.markovchain that implement RandomNumberGenerator Modifier and Type Class Description class
SimpleMC
This is a time-homogeneous Markov chain with a finite state space. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng
Fields in dev.nm.stat.random.rng declared as RandomNumberGenerator Modifier and Type Field Description static RandomNumberGenerator
RNGUtils. SYNC_RNORM
Methods in dev.nm.stat.random.rng that return RandomNumberGenerator Modifier and Type Method Description static RandomNumberGenerator
RNGUtils. synchronizedRNG(RandomNumberGenerator rng)
Returns a synchronized (thread-safe)RandomNumberGenerator
backed by a specified generator.Methods in dev.nm.stat.random.rng with parameters of type RandomNumberGenerator Modifier and Type Method Description static double[]
RNGUtils. nextN(RandomNumberGenerator rng, int n)
Generatesn
random numbers from a given random number generator.static RandomNumberGenerator
RNGUtils. synchronizedRNG(RandomNumberGenerator rng)
Returns a synchronized (thread-safe)RandomNumberGenerator
backed by a specified generator. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.concurrent.cache
Classes in dev.nm.stat.random.rng.concurrent.cache that implement RandomNumberGenerator Modifier and Type Class Description class
ConcurrentCachedRLG
This is a fast thread-safe wrapper for random long generators.class
ConcurrentCachedRNG
This is a fast thread-safe wrapper for random number generators.Constructors in dev.nm.stat.random.rng.concurrent.cache with parameters of type RandomNumberGenerator Constructor Description ConcurrentCachedRNG(RandomNumberGenerator rng)
Construct a new instance which wraps the given random number generator and uses a cache which has 8 entries per available core.ConcurrentCachedRNG(RandomNumberGenerator rng, int cacheSize)
Constructs a new instance which wraps the given random number generator and uses a cache of the specified size. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.concurrent.context
Classes in dev.nm.stat.random.rng.concurrent.context that implement RandomNumberGenerator Modifier and Type Class Description class
ContextRNG<T>
This uniform number generator generates independent sequences of random numbers per context.class
ThreadIDRLG
This uniform number generator generates independent sequences of random numbers per thread, hence thread-safe.class
ThreadIDRNG
This random number generator generates independent sequences of random numbers per thread, hence thread-safe.Methods in dev.nm.stat.random.rng.concurrent.context that return RandomNumberGenerator Modifier and Type Method Description protected RandomNumberGenerator
ContextRNG. getRNG(T context)
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Uses of RandomNumberGenerator in dev.nm.stat.random.rng.multivariate
Constructors in dev.nm.stat.random.rng.multivariate with parameters of type RandomNumberGenerator Constructor Description IID(RandomNumberGenerator rng, int length)
Construct a rvg that outputs vectors that have i.i.d. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
Constructors in dev.nm.stat.random.rng.multivariate.mcmc.hybrid with parameters of type RandomNumberGenerator Constructor Description AbstractHybridMCMC(Vector initialState, RandomNumberGenerator rng)
Constructs a new instance with the given parameters. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
Constructors in dev.nm.stat.random.rng.multivariate.mcmc.metropolis with parameters of type RandomNumberGenerator Constructor Description AbstractMetropolis(Vector initialState, RandomNumberGenerator rng)
Constructs a new instance with the given parameters.MetropolisHastings(RealScalarFunction logf, ProposalFunction proposalFunction, MetropolisHastings.ProposalDensityFunction proposalDensity, Vector initialState, RandomNumberGenerator rng)
Constructs a new instance with the given parameters. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate
Subinterfaces of RandomNumberGenerator in dev.nm.stat.random.rng.univariate Modifier and Type Interface Description interface
RandomLongGenerator
A (pseudo) random number generator that generates a sequence oflong
s that lack any pattern and are uniformly distributed.Classes in dev.nm.stat.random.rng.univariate that implement RandomNumberGenerator Modifier and Type Class Description class
BernoulliTrial
A Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success, p, is the same every time the experiment is conducted.class
BinomialRNG
This random number generator samples from the binomial distribution.class
BurnInRNG
A burn-in random number generator discards the first M samples.class
InverseTransformSampling
Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc.) is a basic method for pseudo-random number sampling, i.e.class
LogNormalRNG
This random number generator samples from the log-normal distribution.class
RayleighRNG
This random number generator samples from the Rayleigh distribution using the inverse transform sampling method.class
ThinRNG
Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.class
WeibullRNG
This random number generator samples from the Weibull distribution using the inverse transform sampling method.Methods in dev.nm.stat.random.rng.univariate with parameters of type RandomNumberGenerator Modifier and Type Method Description static boolean
BernoulliTrial. nextLogTrial(RandomNumberGenerator rng, double p)
Performs a Bernoulli trial that succeeds with probability ep.static boolean
BernoulliTrial. nextTrial(RandomNumberGenerator rng, double p)
Performs a Bernoulli trial that succeeds with probability p.Constructors in dev.nm.stat.random.rng.univariate with parameters of type RandomNumberGenerator Constructor Description BernoulliTrial(RandomNumberGenerator rng, double p)
Creates a new instance that uses the givenRandomNumberGenerator
to do the trial.BurnInRNG(RandomNumberGenerator rvg, int burnInSamples)
Construct a burn-in RNG.ThinRNG(RandomNumberGenerator rng, int m)
Constructs a thinned RNG. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.beta
Subinterfaces of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.beta Modifier and Type Interface Description interface
RandomBetaGenerator
This is a random number generator that generates random deviates according to the Beta distribution.Classes in dev.nm.stat.random.rng.univariate.beta that implement RandomNumberGenerator Modifier and Type Class Description class
Cheng1978
Cheng, 1978, is a new rejection method for generating beta variates.class
VanDerWaerden1969
Deprecated.Cheng1978
is a much better algorithm. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.exp
Subinterfaces of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.exp Modifier and Type Interface Description interface
RandomExpGenerator
This is a random number generator that generates random deviates according to the exponential distribution.Classes in dev.nm.stat.random.rng.univariate.exp that implement RandomNumberGenerator Modifier and Type Class Description class
InverseTransformSamplingExpRNG
This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.class
Ziggurat2000Exp
This implements the ziggurat algorithm to sample from the exponential distribution. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.gamma
Subinterfaces of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.gamma Modifier and Type Interface Description interface
RandomGammaGenerator
This is a random number generator that generates random deviates according to the Gamma distribution.Classes in dev.nm.stat.random.rng.univariate.gamma that implement RandomNumberGenerator 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 RandomNumberGenerator in dev.nm.stat.random.rng.univariate.normal
Subinterfaces of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.normal Modifier and Type Interface Description interface
RandomStandardNormalGenerator
This is a random number generator that generates random deviates according to the standard Normal distribution.Classes in dev.nm.stat.random.rng.univariate.normal that implement RandomNumberGenerator Modifier and Type Class Description class
BoxMuller
The Box-Muller transform (by George Edward Pelham Box and Mervin Edgar Muller 1958) is a pseudo-random number sampling method for generating pairs of independent standard normally distributed (zero expectation, unit variance) random numbers, given a source of uniformly distributed random numbers.class
ConcurrentStandardNormalRNG
class
MarsagliaBray1964
The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables.class
NormalRNG
This is a random number generator that generates random deviates according to the Normal distribution.class
StandardNormalRNG
An alias forZignor2005
to provide a default implementation for sampling from the standard Normal distribution.class
Ziggurat2000
The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.class
Zignor2005
This is an improved version of the Ziggurat algorithm as proposed in the reference. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.normal.truncated
Classes in dev.nm.stat.random.rng.univariate.normal.truncated that implement RandomNumberGenerator 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). -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.poisson
Classes in dev.nm.stat.random.rng.univariate.poisson that implement RandomNumberGenerator Modifier and Type Class Description class
Knuth1969
This is a random number generator that generates random deviates according to the Poisson distribution. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.uniform
Classes in dev.nm.stat.random.rng.univariate.uniform that implement RandomNumberGenerator Modifier and Type Class Description class
MWC8222
Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.class
SHR0
SHR0 is a simple uniform random number generator.class
SHR3
SHR3 is a 3-shift-register generator with period 2^32-1.class
UniformRNG
A pseudo uniform random number generator samples numbers from the unit interval, [0, 1], in such a way that there are equal probabilities of them falling in any same length sub-interval. -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.uniform.linear
Subinterfaces of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.uniform.linear Modifier and Type Interface Description interface
LinearCongruentialGenerator
A linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation.Classes in dev.nm.stat.random.rng.univariate.uniform.linear that implement RandomNumberGenerator Modifier and Type Class Description class
CompositeLinearCongruentialGenerator
A composite generator combines a number of simpleLinearCongruentialGenerator
, such asLehmer
, to form one longer period generator by first summing values and then taking modulus.class
LEcuyer
This is the uniform random number generator recommended by L'Ecuyer in 1996.class
Lehmer
Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in [0, 1].class
MRG
A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form: -
Uses of RandomNumberGenerator in dev.nm.stat.random.rng.univariate.uniform.mersennetwister
Classes in dev.nm.stat.random.rng.univariate.uniform.mersennetwister that implement RandomNumberGenerator Modifier and Type Class Description class
MersenneTwister
Mersenne Twister is one of the best pseudo random number generators available. -
Uses of RandomNumberGenerator in dev.nm.stat.random.sampler.resampler.bootstrap.block
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Uses of RandomNumberGenerator in dev.nm.stat.random.variancereduction
Constructors in dev.nm.stat.random.variancereduction with parameters of type RandomNumberGenerator Constructor Description AntitheticVariates(UnivariateRealFunction f, RandomNumberGenerator X1)
Estimates \(E(f(X_1))\) and use AntitheticVariates.INVERSE as the default antithetic path.AntitheticVariates(UnivariateRealFunction f, RandomNumberGenerator X1, UnivariateRealFunction X2)
Estimates \(E(f(X_1))\), where f is a function of a random variable.ControlVariates(UnivariateRealFunction f, UnivariateRealFunction g, double Eg, double b, RandomNumberGenerator X)
Estimates \(E(f(X_1))\), where f is a function of a random variable.ImportanceSampling(UnivariateRealFunction h, UnivariateRealFunction w, RandomNumberGenerator G)
Uses importance sample to do Monte Carlo integration. -
Uses of RandomNumberGenerator in dev.nm.stat.stochasticprocess.univariate.random
Classes in dev.nm.stat.stochasticprocess.univariate.random that implement RandomNumberGenerator Modifier and Type Class Description class
RandomProcess
This interface represents a univariate random process a.k.a.class
RandomWalk
This is the Random Walk construction of a stochastic process per SDE specification. -
Uses of RandomNumberGenerator in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
Classes in dev.nm.stat.stochasticprocess.univariate.sde.process.ou that implement RandomNumberGenerator Modifier and Type Class Description class
OUSim
This class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process. -
Uses of RandomNumberGenerator in dev.nm.stat.timeseries.linear.univariate.arima
Classes in dev.nm.stat.timeseries.linear.univariate.arima that implement RandomNumberGenerator Modifier and Type Class Description class
ARIMASim
This class simulates an ARIMA (AutoRegressive Integrated Moving Average) process.Constructors in dev.nm.stat.timeseries.linear.univariate.arima with parameters of type RandomNumberGenerator Constructor Description ARIMASim(ARIMAModel arima, double[] lags, double[] innovations, RandomNumberGenerator rng)
Construct an ARIMA model.ARIMASim(ARIMAModel arima, RandomNumberGenerator rng)
Construct an ARIMA model. -
Uses of RandomNumberGenerator in dev.nm.stat.timeseries.linear.univariate.stationaryprocess
Constructors in dev.nm.stat.timeseries.linear.univariate.stationaryprocess with parameters of type RandomNumberGenerator Constructor Description AdditiveModel(double[] trend, double[] seasonality, RandomNumberGenerator rng)
Construct a univariate time series by adding up the components.MultiplicativeModel(double[] trend, double[] seasonality, RandomNumberGenerator rng)
Construct a univariate time series by multiplying the components. -
Uses of RandomNumberGenerator in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch that implement RandomNumberGenerator Modifier and Type Class Description class
GARCHSim
This class simulates the GARCH models of this form.Constructors in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch with parameters of type RandomNumberGenerator Constructor Description GARCHSim(GARCHModel model, double[] z, RandomNumberGenerator rng)
Simulate an GARCH model.GARCHSim(GARCHModel model, RandomNumberGenerator rng)
Simulate an GARCH model. -
Uses of RandomNumberGenerator in tech.nmfin.trend.kst1995
Classes in tech.nmfin.trend.kst1995 that implement RandomNumberGenerator Modifier and Type Class Description class
KnightSatchellTran1995
Implements the Knight-Satchell-Tran model of financial asset returns.
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