pymc.step_methods.BinaryGibbsMetropolis#

class pymc.step_methods.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`(apoint, *args)	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.reset_tuning`()
`BinaryGibbsMetropolis.step`(point)	Perform a single step of the sampler.
`BinaryGibbsMetropolis.stop_tuning`()

Attributes

`name`
`stats_dtypes`	A list containing <=1 dictionary that maps stat names to dtypes.
`stats_dtypes_shapes`	Maps stat names to dtypes and shapes.
`vars`	Variables that the step method is assigned to.