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#Com.numericalmethod.suanshu.stats.random Classes and Interfaces - 96 results found.
| Name | Description | Type | Package | Framework |
| AbstractHybridMCMC | Hybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
| 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 | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
| AntitheticVariates | The 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, for | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
| 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 | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| BinomialRNG | This random number generator samples from the binomial distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| BootstrapEstimator | This class estimates the statistic of a sample using a bootstrap method. | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler | SuanShu |
| 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 | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
| BurnInRNG | A burn-in random number generator discards the first M samples. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| BurnInRVG | A burn-in random number generator discards the first M samples. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| CaseResamplingReplacement | This is the classical bootstrap method described in the reference. | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap | SuanShu |
| Cheng1978 | Cheng, 1978, is a new rejection method for generating beta variates. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.beta | SuanShu |
| CommonRandomNumbers | The common random numbers is a variance reduction technique to apply when we are comparing two random systems, e. | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
| 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 | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
| ConcurrentCachedGenerator | A generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
| ConcurrentCachedGenerator .Generator | Defines a generic generator of type T. | Interface | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
| ConcurrentCachedRLG | This is a fast thread-safe wrapper for random long generators. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
| ConcurrentCachedRNG | This is a fast thread-safe wrapper for random number generators. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
| ConcurrentCachedRVG | This is a fast thread-safe wrapper for random vector generators. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
| ConcurrentStandardNormalRNG | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu | |
| ConstantSeeder | A wrapper that seeds each given seedable random number generator with the given seed(s). | Class | com.numericalmethod.suanshu.stats.random.rng | SuanShu |
| ContextRNG | This uniform number generator generates independent sequences of random numbers per context. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.context | SuanShu |
| ControlVariates | Control 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 unknown | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
| ControlVariates .Estimator | Interface | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu | |
| DynamicCreator | Performs the Dynamic Creation algorithm (DC) to generate parameters for MersenneTwister. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
| DynamicCreatorException | Indicates that a problem has occurred in the dynamic creation process, usually because suitable parameters were not found. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
| ErgodicHybridMCMC | A simple decorator which will randomly vary dt between each sample. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
| Estimator | Gets the expectation of the estimator. | Interface | com.numericalmethod.suanshu.stats.random | SuanShu |
| GaussianProposalFunction | A proposal generator where each perturbation is a random vector, where each element is drawn from a standard Normal distribution, multiplied by a scale matrix. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction | SuanShu |
| GroupResampler | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate | SuanShu | |
| HybridMCMC | This class implements a hybrid MCMC algorithm. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
| HybridMCMCProposalFunction | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction | SuanShu | |
| HypersphereRVG | Generates uniformly distributed points on a unit hypersphere. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| IID | An i. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| ImportanceSampling | Importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution rather than the | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
| InverseTransformSampling | Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| InverseTransformSamplingExpRNG | This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.exp | SuanShu |
| InverseTransformSamplingGammaRNG | This is a pseudo random number generator that samples from the gamma distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
| InverseTransformSamplingTruncatedNormalRNG | A random variate x defined as x = Phi^{-1}( Phi(alpha) + Ucdot(Phi(eta)-Phi(alpha)))sigma + mu | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated | SuanShu |
| Knuth1969 | This is a random number generator that generates random deviates according to the Poisson Generating Poisson-distributed random variables | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.poisson | SuanShu |
| KunduGupta2007 | Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution, | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
| LeapFrogging | The leap-frogging algorithm is a method for simulating Molecular Dynamics, which isSee Also:"Jun S. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
| LeapFrogging .DynamicsState | Contains the entire state (both the position and the momentum) at a given point in time. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
| LEcuyer | This is the uniform random number generator recommended by L'Ecuyer in 1996. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
| Lehmer | Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in xi+1 = (a * xi + c) mod m | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
| LinearCongruentialGenerator | A linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
| LogNormalRNG | This random number generator samples from the log-normal distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| 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 | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
| MarsagliaTsang2000 | Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
| MeanEstimator | Interface | com.numericalmethod.suanshu.stats.random | SuanShu | |
| MersenneExponent | enum MersenneExponentThe value of a Mersenne Exponent p is a parameter for creating a Mersenne-Twister random | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
| MersenneTwister | Mersenne Twister is one of the best pseudo random number generators available. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister | SuanShu |
| MersenneTwisterParam | Immutable parameters for creating a MersenneTwister RNG. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister | SuanShu |
| MersenneTwisterParamSearcher | Searches for Mersenne-Twister parameters. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
| Metropolis | This basic Metropolis implementation assumes using symmetric proposal function. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
| MetropolisHastings | A generalization of the Metropolis algorithm, which allows asymmetric proposal Metropolis-HastingsLiu, Jun S. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
| MetropolisHastings .ProposalDensityFunction | Defines the density of a proposal function, i. | Interface | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
| MetropolisUtils | Utility functions for Metropolis algorithms. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
| MRG | A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form: xi = (a1 * xi-1 + a2 * xi-2 + . | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
| MultinomialRVG | A multinomial distribution puts N objects into K bins according to the bins' probabilities. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| 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 | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
| MultivariateResampler | This is the interface of a multivariate re-sampler method. | Interface | com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate | SuanShu |
| MWC8222 | Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
| NormalRNG | This is a random number generator that generates random deviates according to the NormalSee Also:Wikipedia: Normal | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
| 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 | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| PattonPolitisWhite2009 | This class implements the stationary and circular block bootstrapping method with optimized blockSee Also:Politis, N. | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block | SuanShu |
| PattonPolitisWhite2009 .Type | Returns the enum constant of this type with the specified name. | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block | SuanShu |
| ProposalFunction | A proposal function goes from the current state to the next state, where a state is a vector. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction | SuanShu |
| RandomBetaGenerator | This is a random number generator that generates random deviates according to the Beta distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.beta | SuanShu |
| RandomExpGenerator | This is a random number generator that generates random deviates according to the exponential distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.exp | SuanShu |
| RandomGammaGenerator | This is a random number generator that generates random deviates according to the Gamma distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
| RandomLongGenerator | A (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| RandomNumberGenerator | A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| RandomStandardNormalGenerator | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu | |
| RandomVectorGenerator | A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| RayleighRNG | This random number generator samples from the Rayleigh distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| Resampler | This is the interface of a re-sampler method. | Interface | com.numericalmethod.suanshu.stats.random.sampler.resampler | SuanShu |
| RNGUtils | Provides static methods that wraps random number generators to produce synchronized generators. | Class | com.numericalmethod.suanshu.stats.random.rng | SuanShu |
| 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). | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
| Seedable | A seed-able experiment allow the same experiment to be repeated in exactly the same way. | Interface | com.numericalmethod.suanshu.stats.random | SuanShu |
| SHR0 | SHR0 is a simple uniform random number generator. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
| SHR3 | SHR3 is a 3-shift-register generator with period 2^32-1. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
| StandardNormalRNG | An alias for Zignor2005 to provide a default implementation for sampling from the standard Normal distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
| ThinRNG | Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| ThinRVG | Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| ThreadIDRLG | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.context | SuanShu | |
| ThreadIDRNG | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.context | SuanShu | |
| UniformDistributionOverBox | This random vector generator uniformly samples points over a box region. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| 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. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
| UniformRNG .Method | the pseudo uniform random number generators availableMersenne Twister (recommended) | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
| VanDerWaerden1969 | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.beta | SuanShu | |
| WeibullRNG | This random number generator samples from the Weibull distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
| 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 | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
| 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. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
| Ziggurat2000 | The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
| Ziggurat2000Exp | This implements the ziggurat algorithm to sample from the exponential distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.exp | SuanShu |
| Zignor2005 | This is an improved version of the Ziggurat algorithm as proposed in the reference. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |