Samplers

This submodule contains functions for MCMC and forward sampling.

sample([draws, tune, chains, cores, ...])	Draw samples from the posterior using the given step methods.
sample_prior_predictive([samples, model, ...])	Generate samples from the prior predictive distribution.
sample_posterior_predictive(trace[, model, ...])	Generate forward samples for var_names, conditioned on the posterior samples of variables found in the trace.
draw(vars[, draws, random_seed])	Draw samples for one variable or a list of variables
compute_deterministics(dataset, *[, ...])	Compute model deterministics given a dataset with values for model variables.
init_nuts(*[, init, chains, n_init, model, ...])	Set up the mass matrix initialization for NUTS.
sampling.jax.sample_blackjax_nuts([draws, ...])
sampling.jax.sample_numpyro_nuts([draws, ...])

Step methods

HMC family

NUTS(*args, **kwargs)	A sampler for continuous variables based on Hamiltonian mechanics.
HamiltonianMC(*args, **kwargs)	A sampler for continuous variables based on Hamiltonian mechanics.

Metropolis family

BinaryGibbsMetropolis(*args, **kwargs)	A Metropolis-within-Gibbs step method optimized for binary variables
BinaryMetropolis(*args, **kwargs)	Metropolis-Hastings optimized for binary variables
CategoricalGibbsMetropolis(*args, **kwargs)	A Metropolis-within-Gibbs step method optimized for categorical variables.
CauchyProposal(s)
DEMetropolis(*args, **kwargs)	Differential Evolution Metropolis sampling step.
DEMetropolisZ(*args, **kwargs)	Adaptive Differential Evolution Metropolis sampling step that uses the past to inform jumps.
LaplaceProposal(s)
Metropolis(*args, **kwargs)	Metropolis-Hastings sampling step
MultivariateNormalProposal(s)
NormalProposal(s)
PoissonProposal(s)
UniformProposal(s)

Other step methods

CompoundStep(methods)	Step method composed of a list of several other step methods applied in sequence.
Slice(*args, **kwargs)	Univariate slice sampler step method.