Uses of Interface
dev.nm.stat.random.Seedable

Packages that use Seedable

Package	Description
dev.nm.stat.distribution.discrete
dev.nm.stat.evt.evd.bivariate
dev.nm.stat.evt.evd.univariate.rng
dev.nm.stat.evt.markovchain
dev.nm.stat.evt.timeseries
dev.nm.stat.hmm
dev.nm.stat.hmm.discrete
dev.nm.stat.hmm.mixture
dev.nm.stat.markovchain
dev.nm.stat.random.rng
dev.nm.stat.random.rng.concurrent.cache
dev.nm.stat.random.rng.concurrent.context
dev.nm.stat.random.rng.multivariate
dev.nm.stat.random.rng.multivariate.mcmc.hybrid
dev.nm.stat.random.rng.multivariate.mcmc.metropolis
dev.nm.stat.random.rng.univariate
dev.nm.stat.random.rng.univariate.beta
dev.nm.stat.random.rng.univariate.exp
dev.nm.stat.random.rng.univariate.gamma
dev.nm.stat.random.rng.univariate.normal
dev.nm.stat.random.rng.univariate.normal.truncated
dev.nm.stat.random.rng.univariate.poisson
dev.nm.stat.random.rng.univariate.uniform
dev.nm.stat.random.rng.univariate.uniform.linear
dev.nm.stat.random.rng.univariate.uniform.mersennetwister
dev.nm.stat.random.sampler.resampler
dev.nm.stat.random.sampler.resampler.bootstrap
dev.nm.stat.random.sampler.resampler.bootstrap.block
dev.nm.stat.random.sampler.resampler.multivariate
dev.nm.stat.stochasticprocess.multivariate.random
dev.nm.stat.stochasticprocess.univariate.random
dev.nm.stat.stochasticprocess.univariate.sde.process.ou
dev.nm.stat.test.distribution.pearson
dev.nm.stat.timeseries.linear.multivariate.arima
dev.nm.stat.timeseries.linear.univariate.arima
dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
tech.nmfin.trend.kst1995

Uses of Seedable in dev.nm.stat.distribution.discrete

Classes in dev.nm.stat.distribution.discrete that implement Seedable

Modifier and Type	Class	Description
class	ProbabilityMassSampler<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
interface	BivariateEVD	Bivariate 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
class	AbstractBivariateEVD
class	BivariateEVDAsymmetricLogistic	The bivariate asymmetric logistic model.
class	BivariateEVDAsymmetricMixed	The asymmetric mixed model.
class	BivariateEVDAsymmetricNegativeLogistic	The bivariate asymmetric negative logistic model.
class	BivariateEVDBilogistic	The bilogistic model.
class	BivariateEVDColesTawn	The Coles-Tawn model.
class	BivariateEVDHuslerReiss	The Husler-Reiss model.
class	BivariateEVDLogistic	The bivariate logistic model.
class	BivariateEVDNegativeBilogistic	The negative bilogistic model.
class	BivariateEVDNegativeLogistic	The 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
class	InverseTransformSamplingEVDRNG	Generate 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
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.

Uses of Seedable in dev.nm.stat.evt.timeseries

Classes in dev.nm.stat.evt.timeseries that implement Seedable

Modifier and Type	Class	Description
class	MARMASim	Generate 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
class	HiddenMarkovModel
class	HMMRNG	In 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
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 Seedable in dev.nm.stat.hmm.mixture

Classes in dev.nm.stat.hmm.mixture that implement Seedable

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 Seedable in dev.nm.stat.markovchain

Classes in dev.nm.stat.markovchain that implement Seedable

Modifier and Type	Class	Description
class	SimpleMC	This 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
class	ConstantSeeder<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 interface	ConcurrentCachedGenerator.Generator<T>	Defines a generic generator of type T.

Classes in dev.nm.stat.random.rng.concurrent.cache that implement Seedable

Modifier and Type	Class	Description
class	ConcurrentCachedGenerator<T>	A generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list.
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.
class	ConcurrentCachedRVG	This 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
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.

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
interface	RandomVectorGenerator	A (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
class	BurnInRVG	A burn-in random number generator discards the first M samples.
class	HypersphereRVG	Generates uniformly distributed points on the surface of a hypersphere.
class	IID	An i.i.d.
class	MultinomialRVG	A multinomial distribution puts N objects into K bins according to the bins' probabilities.
class	NormalRVG	A 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.
class	ThinRVG	Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.
class	UniformDistributionOverBox	This 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
class	AbstractHybridMCMC	Hybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation.
class	ErgodicHybridMCMC	A simple decorator which will randomly vary dt between each sample.
class	HybridMCMC	This class implements a hybrid MCMC algorithm.
class	MultipointHybridMCMC	A 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
class	AbstractMetropolis	The 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.
class	Metropolis	This basic Metropolis implementation assumes using symmetric proposal function.
class	MetropolisHastings	A generalization of the Metropolis algorithm, which allows asymmetric proposal functions.
class	RobustAdaptiveMetropolis	A 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
interface	RandomLongGenerator	A (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed.
interface	RandomNumberGenerator	A (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
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.

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
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 Seedable

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 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
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 Seedable

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 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
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 Seedable

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 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
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 Seedable

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 for Zignor2005 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 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
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 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
class	Knuth1969	This 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
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 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
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 Seedable

Modifier and Type	Class	Description
class	CompositeLinearCongruentialGenerator	A 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.
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 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
class	MersenneTwister	Mersenne 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
interface	ObjectResampler<X>	This is the interface of a re-sampler method for objects.
interface	Resampler	This 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
class	CaseResamplingReplacement	This is the classical bootstrap method described in the reference.
class	CaseResamplingReplacementForObject<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
class	PattonPolitisWhite2009	This class implements the stationary and circular block bootstrapping method with optimized block length.
class	PattonPolitisWhite2009ForObject<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
interface	MultivariateResampler	This 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
class	GroupResampler

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
interface	MultivariateRandomRealizationGenerator	This 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
class	MultivariateBrownianRRG	This is the Random Walk construction of a multivariate Brownian motion.
class	MultivariateRandomProcess	This interface represents a multivariate random process a.k.a.
class	MultivariateRandomRealizationOfRandomProcess	This class generates random realizations from a multivariate random/stochastic process.
class	MultivariateRandomWalk	This 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
interface	RandomRealizationGenerator	This 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
class	RandomProcess	This interface represents a univariate random process a.k.a.
class	RandomRealizationOfRandomProcess	This class generates random realizations from a random/stochastic process.
class	RandomWalk	This 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
class	OUSim	This 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
class	AS159	Algorithm 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
class	VARIMASim	This 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
class	ARIMASim	This 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
class	GARCHSim	This 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
class	GroupResamplerFactory	Creates 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
class	KnightSatchellTran1995	Implements the Knight-Satchell-Tran model of financial asset returns.