Uses of Interface
dev.nm.stat.random.Seedable
-
-
Uses of Seedable in dev.nm.stat.distribution.discrete
Classes in dev.nm.stat.distribution.discrete that implement Seedable Modifier and Type Class Description classProbabilityMassSampler<X>A random sampler that is constructed ad-hoc from a list of values and their probabilities. -
Uses of Seedable in dev.nm.stat.evt.evd.bivariate
Subinterfaces of Seedable in dev.nm.stat.evt.evd.bivariate Modifier and Type Interface Description interfaceBivariateEVDBivariate Extreme Value (BEV) distribution is the joint distribution of component-wise maxima of two-dimensional iid random vectors.Classes in dev.nm.stat.evt.evd.bivariate that implement Seedable Modifier and Type Class Description classAbstractBivariateEVDclassBivariateEVDAsymmetricLogisticThe bivariate asymmetric logistic model.classBivariateEVDAsymmetricMixedThe asymmetric mixed model.classBivariateEVDAsymmetricNegativeLogisticThe bivariate asymmetric negative logistic model.classBivariateEVDBilogisticThe bilogistic model.classBivariateEVDColesTawnThe Coles-Tawn model.classBivariateEVDHuslerReissThe Husler-Reiss model.classBivariateEVDLogisticThe bivariate logistic model.classBivariateEVDNegativeBilogisticThe negative bilogistic model.classBivariateEVDNegativeLogisticThe bivariate negative logistic model. -
Uses of Seedable in dev.nm.stat.evt.evd.univariate.rng
Classes in dev.nm.stat.evt.evd.univariate.rng that implement Seedable Modifier and Type Class Description classInverseTransformSamplingEVDRNGGenerate random numbers according to a given univariate extreme value distribution, by inverse transform sampling. -
Uses of Seedable in dev.nm.stat.evt.markovchain
Classes in dev.nm.stat.evt.markovchain that implement Seedable Modifier and Type Class Description classExtremeValueMCSimulation of first order extreme value Markov chains such that each pair of consecutive values has the dependence structure of given bivariate extreme value distributions. -
Uses of Seedable in dev.nm.stat.evt.timeseries
Classes in dev.nm.stat.evt.timeseries that implement Seedable Modifier and Type Class Description classMARMASimGenerate random numbers based on a given MARMA model. -
Uses of Seedable in dev.nm.stat.hmm
Classes in dev.nm.stat.hmm that implement Seedable Modifier and Type Class Description classHiddenMarkovModelclassHMMRNGIn a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. -
Uses of Seedable in dev.nm.stat.hmm.discrete
Classes in dev.nm.stat.hmm.discrete that implement Seedable Modifier and Type Class Description classBaumWelchThis implementation trains an HMM model by observations using the Baum–Welch algorithm.classDiscreteHMMThis is the discrete hidden Markov model as defined by Rabiner. -
Uses of Seedable in dev.nm.stat.hmm.mixture
Classes in dev.nm.stat.hmm.mixture that implement Seedable Modifier and Type Class Description classMixtureHMMThis is the mixture hidden Markov model (HMM).classMixtureHMMEMThe 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 Seedable in dev.nm.stat.markovchain
Classes in dev.nm.stat.markovchain that implement Seedable Modifier and Type Class Description classSimpleMCThis is a time-homogeneous Markov chain with a finite state space. -
Uses of Seedable in dev.nm.stat.random.rng
Classes in dev.nm.stat.random.rng with type parameters of type Seedable Modifier and Type Class Description classConstantSeeder<T extends Seedable>A wrapper that seeds each given seedable random number generator with the given seed(s). -
Uses of Seedable in dev.nm.stat.random.rng.concurrent.cache
Subinterfaces of Seedable in dev.nm.stat.random.rng.concurrent.cache Modifier and Type Interface Description static interfaceConcurrentCachedGenerator.Generator<T>Defines a generic generator of typeT.Classes in dev.nm.stat.random.rng.concurrent.cache that implement Seedable Modifier and Type Class Description classConcurrentCachedGenerator<T>A generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list.classConcurrentCachedRLGThis is a fast thread-safe wrapper for random long generators.classConcurrentCachedRNGThis is a fast thread-safe wrapper for random number generators.classConcurrentCachedRVGThis is a fast thread-safe wrapper for random vector generators. -
Uses of Seedable in dev.nm.stat.random.rng.concurrent.context
Classes in dev.nm.stat.random.rng.concurrent.context that implement Seedable Modifier and Type Class Description classContextRNG<T>This uniform number generator generates independent sequences of random numbers per context.classThreadIDRLGThis uniform number generator generates independent sequences of random numbers per thread, hence thread-safe.classThreadIDRNGThis random number generator generates independent sequences of random numbers per thread, hence thread-safe. -
Uses of Seedable in dev.nm.stat.random.rng.multivariate
Subinterfaces of Seedable in dev.nm.stat.random.rng.multivariate Modifier and Type Interface Description interfaceRandomVectorGeneratorA (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.Classes in dev.nm.stat.random.rng.multivariate that implement Seedable Modifier and Type Class Description classBurnInRVGA burn-in random number generator discards the first M samples.classHypersphereRVGGenerates uniformly distributed points on the surface of a hypersphere.classIIDAn i.i.d.classMultinomialRVGA multinomial distribution puts N objects into K bins according to the bins' probabilities.classNormalRVGA 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.classThinRVGThinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.classUniformDistributionOverBoxThis random vector generator uniformly samples points over a box region. -
Uses of Seedable in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
Classes in dev.nm.stat.random.rng.multivariate.mcmc.hybrid that implement Seedable Modifier and Type Class Description classAbstractHybridMCMCHybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation.classErgodicHybridMCMCA simple decorator which will randomly vary dt between each sample.classHybridMCMCThis class implements a hybrid MCMC algorithm.classMultipointHybridMCMCA multi-point Hybrid Monte Carlo is an extension of HybridMCMC, where during the proposal generation instead of considering only the last configuration after the dynamics simulation, we pick a proposal from a window of the last M configurations. -
Uses of Seedable in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
Classes in dev.nm.stat.random.rng.multivariate.mcmc.metropolis that implement Seedable Modifier and Type Class Description classAbstractMetropolisThe 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.classMetropolisThis basic Metropolis implementation assumes using symmetric proposal function.classMetropolisHastingsA generalization of the Metropolis algorithm, which allows asymmetric proposal functions.classRobustAdaptiveMetropolisA variation of Metropolis, that uses the estimated covariance of the target distribution in the proposal distribution, based on a paper by Vihola (2011). -
Uses of Seedable in dev.nm.stat.random.rng.univariate
Subinterfaces of Seedable in dev.nm.stat.random.rng.univariate Modifier and Type Interface Description interfaceRandomLongGeneratorA (pseudo) random number generator that generates a sequence oflongs that lack any pattern and are uniformly distributed.interfaceRandomNumberGeneratorA (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.Classes in dev.nm.stat.random.rng.univariate that implement Seedable Modifier and Type Class Description classBernoulliTrialA 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.classBinomialRNGThis random number generator samples from the binomial distribution.classBurnInRNGA burn-in random number generator discards the first M samples.classInverseTransformSamplingInverse 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.classLogNormalRNGThis random number generator samples from the log-normal distribution.classRayleighRNGThis random number generator samples from the Rayleigh distribution using the inverse transform sampling method.classThinRNGThinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.classWeibullRNGThis random number generator samples from the Weibull distribution using the inverse transform sampling method. -
Uses of Seedable in dev.nm.stat.random.rng.univariate.beta
Subinterfaces of Seedable in dev.nm.stat.random.rng.univariate.beta Modifier and Type Interface Description interfaceRandomBetaGeneratorThis 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 Seedable Modifier and Type Class Description classCheng1978Cheng, 1978, is a new rejection method for generating beta variates.classVanDerWaerden1969Deprecated.Cheng1978is a much better algorithm. -
Uses of Seedable in dev.nm.stat.random.rng.univariate.exp
Subinterfaces of Seedable in dev.nm.stat.random.rng.univariate.exp Modifier and Type Interface Description interfaceRandomExpGeneratorThis 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 Seedable Modifier and Type Class Description classInverseTransformSamplingExpRNGThis is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.classZiggurat2000ExpThis implements the ziggurat algorithm to sample from the exponential distribution. -
Uses of Seedable in dev.nm.stat.random.rng.univariate.gamma
Subinterfaces of Seedable in dev.nm.stat.random.rng.univariate.gamma Modifier and Type Interface Description interfaceRandomGammaGeneratorThis 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 Seedable Modifier and Type Class Description classInverseTransformSamplingGammaRNGDeprecated.There exist much more efficient algorithms.classKunduGupta2007Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution, when the shape parameter lies between 0 and 1.classMarsagliaTsang2000Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate.classXiTanLiu2010aXi, 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.classXiTanLiu2010bXi, 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 Seedable in dev.nm.stat.random.rng.univariate.normal
Subinterfaces of Seedable in dev.nm.stat.random.rng.univariate.normal Modifier and Type Interface Description interfaceRandomStandardNormalGeneratorThis 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 Seedable Modifier and Type Class Description classBoxMullerThe 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.classConcurrentStandardNormalRNGclassMarsagliaBray1964The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables.classNormalRNGThis is a random number generator that generates random deviates according to the Normal distribution.classStandardNormalRNGAn alias forZignor2005to provide a default implementation for sampling from the standard Normal distribution.classZiggurat2000The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.classZignor2005This is an improved version of the Ziggurat algorithm as proposed in the reference. -
Uses of Seedable in dev.nm.stat.random.rng.univariate.normal.truncated
Classes in dev.nm.stat.random.rng.univariate.normal.truncated that implement Seedable Modifier and Type Class Description classInverseTransformSamplingTruncatedNormalRNGA 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 Seedable in dev.nm.stat.random.rng.univariate.poisson
Classes in dev.nm.stat.random.rng.univariate.poisson that implement Seedable Modifier and Type Class Description classKnuth1969This is a random number generator that generates random deviates according to the Poisson distribution. -
Uses of Seedable in dev.nm.stat.random.rng.univariate.uniform
Classes in dev.nm.stat.random.rng.univariate.uniform that implement Seedable Modifier and Type Class Description classMWC8222Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.classSHR0SHR0 is a simple uniform random number generator.classSHR3SHR3 is a 3-shift-register generator with period 2^32-1.classUniformRNGA 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 Seedable in dev.nm.stat.random.rng.univariate.uniform.linear
Subinterfaces of Seedable in dev.nm.stat.random.rng.univariate.uniform.linear Modifier and Type Interface Description interfaceLinearCongruentialGeneratorA 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 Seedable Modifier and Type Class Description classCompositeLinearCongruentialGeneratorA composite generator combines a number of simpleLinearCongruentialGenerator, such asLehmer, to form one longer period generator by first summing values and then taking modulus.classLEcuyerThis is the uniform random number generator recommended by L'Ecuyer in 1996.classLehmerLehmer proposed a general linear congruential generator that generates pseudo-random numbers in [0, 1].classMRGA Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form: -
Uses of Seedable in dev.nm.stat.random.rng.univariate.uniform.mersennetwister
Classes in dev.nm.stat.random.rng.univariate.uniform.mersennetwister that implement Seedable Modifier and Type Class Description classMersenneTwisterMersenne Twister is one of the best pseudo random number generators available. -
Uses of Seedable in dev.nm.stat.random.sampler.resampler
Subinterfaces of Seedable in dev.nm.stat.random.sampler.resampler Modifier and Type Interface Description interfaceObjectResampler<X>This is the interface of a re-sampler method for objects.interfaceResamplerThis is the interface of a re-sampler method. -
Uses of Seedable in dev.nm.stat.random.sampler.resampler.bootstrap
Classes in dev.nm.stat.random.sampler.resampler.bootstrap that implement Seedable Modifier and Type Class Description classCaseResamplingReplacementThis is the classical bootstrap method described in the reference.classCaseResamplingReplacementForObject<X>This is the classical bootstrap method described in the reference. -
Uses of Seedable in dev.nm.stat.random.sampler.resampler.bootstrap.block
Classes in dev.nm.stat.random.sampler.resampler.bootstrap.block that implement Seedable Modifier and Type Class Description classPattonPolitisWhite2009This class implements the stationary and circular block bootstrapping method with optimized block length.classPattonPolitisWhite2009ForObject<X>This class implements the stationary and circular block bootstrapping method with optimized block length. -
Uses of Seedable in dev.nm.stat.random.sampler.resampler.multivariate
Subinterfaces of Seedable in dev.nm.stat.random.sampler.resampler.multivariate Modifier and Type Interface Description interfaceMultivariateResamplerThis is the interface of a multivariate re-sampler method.Classes in dev.nm.stat.random.sampler.resampler.multivariate that implement Seedable Modifier and Type Class Description classGroupResampler -
Uses of Seedable in dev.nm.stat.stochasticprocess.multivariate.random
Subinterfaces of Seedable in dev.nm.stat.stochasticprocess.multivariate.random Modifier and Type Interface Description interfaceMultivariateRandomRealizationGeneratorThis interface defines a generator to construct random realizations from a multivariate stochastic process.Classes in dev.nm.stat.stochasticprocess.multivariate.random that implement Seedable Modifier and Type Class Description classMultivariateBrownianRRGThis is the Random Walk construction of a multivariate Brownian motion.classMultivariateRandomProcessThis interface represents a multivariate random process a.k.a.classMultivariateRandomRealizationOfRandomProcessThis class generates random realizations from a multivariate random/stochastic process.classMultivariateRandomWalkThis is the Random Walk construction of a multivariate stochastic process per SDE specification. -
Uses of Seedable in dev.nm.stat.stochasticprocess.univariate.random
Subinterfaces of Seedable in dev.nm.stat.stochasticprocess.univariate.random Modifier and Type Interface Description interfaceRandomRealizationGeneratorThis interface defines a generator to construct random realizations from a univariate stochastic process.Classes in dev.nm.stat.stochasticprocess.univariate.random that implement Seedable Modifier and Type Class Description classRandomProcessThis interface represents a univariate random process a.k.a.classRandomRealizationOfRandomProcessThis class generates random realizations from a random/stochastic process.classRandomWalkThis is the Random Walk construction of a stochastic process per SDE specification. -
Uses of Seedable in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
Classes in dev.nm.stat.stochasticprocess.univariate.sde.process.ou that implement Seedable Modifier and Type Class Description classOUSimThis class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process. -
Uses of Seedable in dev.nm.stat.test.distribution.pearson
Classes in dev.nm.stat.test.distribution.pearson that implement Seedable Modifier and Type Class Description classAS159Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums. -
Uses of Seedable in dev.nm.stat.timeseries.linear.multivariate.arima
Classes in dev.nm.stat.timeseries.linear.multivariate.arima that implement Seedable Modifier and Type Class Description classVARIMASimThis class simulates a multivariate ARIMA process. -
Uses of Seedable in dev.nm.stat.timeseries.linear.univariate.arima
Classes in dev.nm.stat.timeseries.linear.univariate.arima that implement Seedable Modifier and Type Class Description classARIMASimThis class simulates an ARIMA (AutoRegressive Integrated Moving Average) process. -
Uses of Seedable in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch that implement Seedable Modifier and Type Class Description classGARCHSimThis class simulates the GARCH models of this form. -
Uses of Seedable in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
Classes in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler that implement Seedable Modifier and Type Class Description classGroupResamplerFactoryCreates re-samplers that do re-sampling for the whole group of stocks together. -
Uses of Seedable in tech.nmfin.trend.kst1995
Classes in tech.nmfin.trend.kst1995 that implement Seedable Modifier and Type Class Description classKnightSatchellTran1995Implements the Knight-Satchell-Tran model of financial asset returns.
-