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#Com.numericalmethod.suanshu.stats.random Classes and Interfaces - 96 results found.
NameDescriptionTypePackageFramework
AbstractHybridMCMCHybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShu
AbstractMetropolisThe Metropolis algorithm is a Markov Chain Monte Carlo algorithm, which requires only a function f proportional to the PDF from which we wish to sample.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShu
AntitheticVariatesThe antithetic variates technique consists, for every sample path obtained, in taking its antithetic path - that is given a path (varepsilon_1,dots,varepsilon_M) to also take, forClasscom.numericalmethod.suanshu.stats.random.variancereductionSuanShu
BernoulliTrialA 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 timeClasscom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
BinomialRNGThis random number generator samples from the binomial distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
BootstrapEstimatorThis class estimates the statistic of a sample using a bootstrap method.Classcom.numericalmethod.suanshu.stats.random.sampler.resamplerSuanShu
BoxMullerThe 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 standardClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu
BurnInRNGA burn-in random number generator discards the first M samples.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
BurnInRVGA burn-in random number generator discards the first M samples.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
CaseResamplingReplacementThis is the classical bootstrap method described in the reference.Classcom.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrapSuanShu
Cheng1978Cheng, 1978, is a new rejection method for generating beta variates.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.betaSuanShu
CommonRandomNumbersThe common random numbers is a variance reduction technique to apply when we are comparing two random systems, e.Classcom.numericalmethod.suanshu.stats.random.variancereductionSuanShu
CompositeLinearCongruentialGeneratorA composite generator combines a number of simple LinearCongruentialGenerator, such as Lehmer, to form one longer period generator by first summing values and then taking modulus.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShu
ConcurrentCachedGeneratorA generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShu
ConcurrentCachedGenerator .GeneratorDefines a generic generator of type T.Interfacecom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShu
ConcurrentCachedRLGThis is a fast thread-safe wrapper for random long generators.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShu
ConcurrentCachedRNGThis is a fast thread-safe wrapper for random number generators.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShu
ConcurrentCachedRVGThis is a fast thread-safe wrapper for random vector generators.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.cacheSuanShu
ConcurrentStandardNormalRNGClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu
ConstantSeederA wrapper that seeds each given seedable random number generator with the given seed(s).Classcom.numericalmethod.suanshu.stats.random.rngSuanShu
ContextRNGThis uniform number generator generates independent sequences of random numbers per context.Classcom.numericalmethod.suanshu.stats.random.rng.concurrent.contextSuanShu
ControlVariatesControl variates method is a variance reduction technique that exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknownClasscom.numericalmethod.suanshu.stats.random.variancereductionSuanShu
ControlVariates .EstimatorInterfacecom.numericalmethod.suanshu.stats.random.variancereductionSuanShu
DynamicCreatorPerforms the Dynamic Creation algorithm (DC) to generate parameters for MersenneTwister.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShu
DynamicCreatorExceptionIndicates that a problem has occurred in the dynamic creation process, usually because suitable parameters were not found.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShu
ErgodicHybridMCMCA simple decorator which will randomly vary dt between each sample.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShu
EstimatorGets the expectation of the estimator.Interfacecom.numericalmethod.suanshu.stats.randomSuanShu
GaussianProposalFunctionA proposal generator where each perturbation is a random vector, where each element is drawn from a standard Normal distribution, multiplied by a scale matrix.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunctionSuanShu
GroupResamplerClasscom.numericalmethod.suanshu.stats.random.sampler.resampler.multivariateSuanShu
HybridMCMCThis class implements a hybrid MCMC algorithm.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShu
HybridMCMCProposalFunctionClasscom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunctionSuanShu
HypersphereRVGGenerates uniformly distributed points on a unit hypersphere.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
IIDAn i.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
ImportanceSamplingImportance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution rather than theClasscom.numericalmethod.suanshu.stats.random.variancereductionSuanShu
InverseTransformSamplingInverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
InverseTransformSamplingExpRNGThis is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.expSuanShu
InverseTransformSamplingGammaRNGThis is a pseudo random number generator that samples from the gamma distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShu
InverseTransformSamplingTruncatedNormalRNGA random variate x defined as x = Phi^{-1}( Phi(alpha) + Ucdot(Phi(eta)-Phi(alpha)))sigma + muClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncatedSuanShu
Knuth1969This is a random number generator that generates random deviates according to the Poisson Generating Poisson-distributed random variablesClasscom.numericalmethod.suanshu.stats.random.rng.univariate.poissonSuanShu
KunduGupta2007Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution,Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShu
LeapFroggingThe leap-frogging algorithm is a method for simulating Molecular Dynamics, which isSee Also:"Jun S.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShu
LeapFrogging .DynamicsStateContains the entire state (both the position and the momentum) at a given point in time.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShu
LEcuyerThis is the uniform random number generator recommended by L'Ecuyer in 1996.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShu
LehmerLehmer proposed a general linear congruential generator that generates pseudo-random numbers in xi+1 = (a * xi + c) mod mClasscom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShu
LinearCongruentialGeneratorA linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShu
LogNormalRNGThis random number generator samples from the log-normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
MarsagliaBray1964The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu
MarsagliaTsang2000Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShu
MeanEstimatorInterfacecom.numericalmethod.suanshu.stats.randomSuanShu
MersenneExponentenum MersenneExponentThe value of a Mersenne Exponent p is a parameter for creating a Mersenne-Twister randomClasscom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShu
MersenneTwisterMersenne Twister is one of the best pseudo random number generators available.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwisterSuanShu
MersenneTwisterParamImmutable parameters for creating a MersenneTwister RNG.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwisterSuanShu
MersenneTwisterParamSearcherSearches for Mersenne-Twister parameters.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreationSuanShu
MetropolisThis basic Metropolis implementation assumes using symmetric proposal function.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShu
MetropolisHastingsA generalization of the Metropolis algorithm, which allows asymmetric proposal Metropolis-HastingsLiu, Jun S.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShu
MetropolisHastings .ProposalDensityFunctionDefines the density of a proposal function, i.Interfacecom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShu
MetropolisUtilsUtility functions for Metropolis algorithms.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShu
MRGA Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form: xi = (a1 * xi-1 + a2 * xi-2 + .Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linearSuanShu
MultinomialRVGA multinomial distribution puts N objects into K bins according to the bins' probabilities.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
MultipointHybridMCMCA multi-point Hybrid Monte Carlo is an extension of HybridMCMC, where during the proposal generation instead of considering only the last configuration after the dynamicsClasscom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybridSuanShu
MultivariateResamplerThis is the interface of a multivariate re-sampler method.Interfacecom.numericalmethod.suanshu.stats.random.sampler.resampler.multivariateSuanShu
MWC8222Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShu
NormalRNGThis is a random number generator that generates random deviates according to the NormalSee Also:Wikipedia: NormalClasscom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu
NormalRVGA multivariate Normal random vector is said to be p-variate normally distributed if every linear combination of its p components has a univariate normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
PattonPolitisWhite2009This class implements the stationary and circular block bootstrapping method with optimized blockSee Also:Politis, N.Classcom.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.blockSuanShu
PattonPolitisWhite2009 .TypeReturns the enum constant of this type with the specified name.Classcom.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.blockSuanShu
ProposalFunctionA proposal function goes from the current state to the next state, where a state is a vector.Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunctionSuanShu
RandomBetaGeneratorThis is a random number generator that generates random deviates according to the Beta distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.betaSuanShu
RandomExpGeneratorThis is a random number generator that generates random deviates according to the exponential distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.expSuanShu
RandomGammaGeneratorThis is a random number generator that generates random deviates according to the Gamma distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShu
RandomLongGeneratorA (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
RandomNumberGeneratorA (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.Interfacecom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
RandomStandardNormalGeneratorInterfacecom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu
RandomVectorGeneratorA (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.Interfacecom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
RayleighRNGThis random number generator samples from the Rayleigh distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
ResamplerThis is the interface of a re-sampler method.Interfacecom.numericalmethod.suanshu.stats.random.sampler.resamplerSuanShu
RNGUtilsProvides static methods that wraps random number generators to produce synchronized generators.Classcom.numericalmethod.suanshu.stats.random.rngSuanShu
RobustAdaptiveMetropolisA variation of Metropolis, that uses the estimated covariance of the target distribution in the proposal distribution, based on a paper by Vihola (2011).Classcom.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolisSuanShu
SeedableA seed-able experiment allow the same experiment to be repeated in exactly the same way.Interfacecom.numericalmethod.suanshu.stats.randomSuanShu
SHR0SHR0 is a simple uniform random number generator.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShu
SHR3SHR3 is a 3-shift-register generator with period 2^32-1.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShu
StandardNormalRNGAn alias for Zignor2005 to provide a default implementation for sampling from the standard Normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu
ThinRNGThinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
ThinRVGThinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
ThreadIDRLGClasscom.numericalmethod.suanshu.stats.random.rng.concurrent.contextSuanShu
ThreadIDRNGClasscom.numericalmethod.suanshu.stats.random.rng.concurrent.contextSuanShu
UniformDistributionOverBoxThis random vector generator uniformly samples points over a box region.Classcom.numericalmethod.suanshu.stats.random.rng.multivariateSuanShu
UniformRNGA 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.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShu
UniformRNG .Methodthe pseudo uniform random number generators availableMersenne Twister (recommended)Classcom.numericalmethod.suanshu.stats.random.rng.univariate.uniformSuanShu
VanDerWaerden1969Classcom.numericalmethod.suanshu.stats.random.rng.univariate.betaSuanShu
WeibullRNGThis random number generator samples from the Weibull distribution using the inverse transform sampling method.Classcom.numericalmethod.suanshu.stats.random.rng.univariateSuanShu
XiTanLiu2010aXi, 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.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShu
XiTanLiu2010bXi, 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.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.gammaSuanShu
Ziggurat2000The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu
Ziggurat2000ExpThis implements the ziggurat algorithm to sample from the exponential distribution.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.expSuanShu
Zignor2005This is an improved version of the Ziggurat algorithm as proposed in the reference.Classcom.numericalmethod.suanshu.stats.random.rng.univariate.normalSuanShu