pymc.step_methods.BinaryMetropolis#

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

Metropolis-Hastings optimized for binary variables

Parameters:

vars: list: List of value variables for sampler
scaling: scalar or array: Initial scale factor for proposal. Defaults to 1.
tune: bool: Flag for tuning. Defaults to True.
tune_interval: int: The frequency of tuning. Defaults to 100 iterations.
model: PyMC Model: Optional model for sampling step. Defaults to None (taken from context).

Methods

`BinaryMetropolis.__init__`(vars[, scaling, ...])
`BinaryMetropolis.astep`(apoint, *args)	Perform a single sample step in a raveled and concatenated parameter space.
`BinaryMetropolis.competence`(var)	BinaryMetropolis is only suitable for binary (bool) and Categorical variables with k=1.
`BinaryMetropolis.step`(point)	Perform a single step of the sampler.
`BinaryMetropolis.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.