MCMC Samplers

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