pymc.BinaryGibbsMetropolis#

class pymc.BinaryGibbsMetropolis(*args, **kwargs)[source]#

A Metropolis-within-Gibbs step method optimized for binary variables

Parameters
vars: list

List of value variables for sampler

order: list or ‘random’

List of integers indicating the Gibbs update order e.g., [0, 2, 1, …]. Default is random

transit_p: float

The diagonal of the transition kernel. A value > .5 gives anticorrelated proposals, which resulting in more efficient antithetical sampling. Default is 0.8

model: PyMC Model

Optional model for sampling step. Defaults to None (taken from context).

Methods

BinaryGibbsMetropolis.__init__(vars[, ...])

BinaryGibbsMetropolis.astep(q0, logp)

Perform a single sample step in a raveled and concatenated parameter space.

BinaryGibbsMetropolis.competence(var)

BinaryMetropolis is only suitable for Bernoulli and Categorical variables with k=2.

BinaryGibbsMetropolis.step(point)

Perform a single step of the sampler.

BinaryGibbsMetropolis.stop_tuning()

Attributes

generates_stats

name

stats_dtypes

vars