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#Com.numericalmethod.suanshu.stats.random.rng.multivariate Classes and Interfaces - 23 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 |
| BurnInRVG | A burn-in random number generator discards the first M samples. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
| ErgodicHybridMCMC | A simple decorator which will randomly vary dt between each sample. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| RandomVectorGenerator | A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.multivariate | 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 |
| 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 |
| UniformDistributionOverBox | This random vector generator uniformly samples points over a box region. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |