pymc.CategoricalGibbsMetropolis#

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

A Metropolis-within-Gibbs step method optimized for categorical variables.

This step method works for Bernoulli variables as well, but it is not optimized for them, like BinaryGibbsMetropolis is. Step method supports two types of proposals: A uniform proposal and a proportional proposal, which was introduced by Liu in his 1996 technical report “Metropolized Gibbs Sampler: An Improvement”.

Methods

CategoricalGibbsMetropolis.__init__(vars[, ...])

CategoricalGibbsMetropolis.astep(q0, logp)

Perform a single sample step in a raveled and concatenated parameter space.

CategoricalGibbsMetropolis.astep_prop(q0, logp)

CategoricalGibbsMetropolis.astep_unif(q0, logp)

CategoricalGibbsMetropolis.competence(var)

CategoricalGibbsMetropolis is only suitable for Bernoulli and Categorical variables.

CategoricalGibbsMetropolis.metropolis_proportional(q, ...)

CategoricalGibbsMetropolis.step(point)

Perform a single step of the sampler.

CategoricalGibbsMetropolis.stop_tuning()

Attributes

generates_stats

name

stats_dtypes

vars