mcmc¶
MCMC (Markov chain Monte Carlo) and related algorithms.
Overview¶
This module contains various classes that implement MCMC samplers:
MCMC
: the base class for all MCMC samplers;GenericRWHM
: base class for random-walk Hastings-Metropolis;GenericGibbs
: base class for Gibbs samplers;PMMH
,ParticleGibbs
: base classes for the PMCMC (particle MCMC algorithms) with the same name.
For instance, here is how to run 200 iterations of an adaptive random-walk sampler:
# ...
# define some_static_model, some_prior
# ...
my_mcmc = BasicRWHM(model=some_static_model, prior=some_prior, niter=200,
adaptive=True)
my_mcmc.run()
Upon completion, object my_mcmc
have an attribute called chain
, which
is a ThetaParticles
object (see module smc_samplers). In particular,
my_mcmc.chain
has the following attributes:
theta
: a structured array that contains the 200 simulated parameters;lpost
: an array that contains the log-posterior density at these 200 parameters.
See the dedicated notebook tutorial (on Bayesian inference for state-space models) for more examples and explanations.
Module summary¶
MCMC |
MCMC base class. |
GenericRWHM |
Base class for random walk Hasting-Metropolis samplers. |
GenericGibbs |
Generic Gibbs sampler for a state-space model. |
PMMH |
Particle Marginal Metropolis Hastings. |
ParticleGibbs |
Particle Gibbs sampler (abstract class). |