pymc.step_methods.BinaryMetropolis#

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

Metropolis-Hastings optimized for binary variables.

Unlike BinaryGibbsMetropolis, this step sampler proposes an update for all variable dimensions at once.

This will perform a single logp evaluation per step, at the expense of a lower acceptance rate when the posteriors of the binary variables are highly correlated.

The BinaryGibbsMetropolis (not this one) is the default step sampler 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).
rng: RandomGenerator: An object that can produce be used to produce the step method’s Generator object. Refer to pymc.util.get_random_generator() for more information.

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.set_rng`(rng)
`BinaryMetropolis.step`(point)	Perform a single step of the sampler.
`BinaryMetropolis.stop_tuning`()

Attributes

`name`
`sampling_state`
`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.