pymc.step_methods.BinaryGibbsMetropolis#
- class pymc.step_methods.BinaryGibbsMetropolis(*args, **kwargs)[source]#
A Metropolis-within-Gibbs step method optimized for binary variables.
Unlike BinaryMetropolis, this step sampler proposes a variable dimension update at a time.
This will increase acceptance rate when the posteriors of the binary variables are highly correlated, at the expense of doing more logp evaluations per step.
This is the default step sampler 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).
- rng: RandomGenerator
An object that can produce be used to produce the step method’s
Generator
object. Refer topymc.util.get_random_generator()
for more information.
Methods
BinaryGibbsMetropolis.__init__
(vars, *[, ...])BinaryGibbsMetropolis.astep
(apoint, *args)Perform a single sample step in a raveled and concatenated parameter space.
BinaryMetropolis is only suitable for Bernoulli and Categorical variables with k=2.
BinaryGibbsMetropolis.step
(point)Perform a single step of the sampler.
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.