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#Com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis Classes and Interfaces - 6 results found.
| Name | Description | Type | Package | Framework |
| 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 |
| 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 |
| 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 |