MCMC SamplersΒΆ
Pints provides a number of MCMC methods, all implementing the MCMC
interface, that can be used to sample from an unknown
PDF
(usually a Bayesian
Posterior
).
- Running an MCMC routine
- MCMC Sampler base classes
- Adaptive Covariance MC
- Differential Evolution MCMC
- Dram ACMC
- DreamMCMC
- Dual Averaging
- EmceeHammerMCMC
- Haario ACMC
- Haario Bardenet ACMC
- Hamiltonian MCMC
- Metropolis-Adjusted Langevin Algorithm (MALA) MCMC
- Metropolis Random Walk MCMC
- Monomial-Gamma Hamiltonian MCMC
- No-U-Turn MCMC Sampler
- Population MCMC
- Rao-Blackwell ACMC
- Relativistic MCMC
- Slice Sampling - Doubling MCMC
- Slice Sampling - Rank Shrinking MCMC
- Slice Sampling - Stepout MCMC
- MCMC Summary