pymc.BinaryMetropolis#

class pymc.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(q0, logp)

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

generates_stats

name

stats_dtypes

vars