GibbsSampling

class pgmpy.sampling.GibbsSampling(model=None)

Bases: MarkovChain

Class for performing Gibbs sampling.

Parameters:

model: DiscreteBayesianNetwork or DiscreteMarkovNetwork

Model from which variables are inherited and transition probabilities computed.

Examples

Initialization from a DiscreteBayesianNetwork object:

>>> from pgmpy.factors.discrete import TabularCPD
>>> from pgmpy.models import DiscreteBayesianNetwork
>>> intel_cpd = TabularCPD("intel", 2, [[0.7], [0.3]])
>>> sat_cpd = TabularCPD(
...     "sat", 2, [[0.95, 0.2], [0.05, 0.8]], evidence=["intel"], evidence_card=[2]
... )
>>> student = DiscreteBayesianNetwork()
>>> student.add_nodes_from(["intel", "sat"])
>>> student.add_edge("intel", "sat")
>>> student.add_cpds(intel_cpd, sat_cpd)
>>> from pgmpy.sampling import GibbsSampling
>>> gibbs_chain = GibbsSampling(student)
>>> gibbs_chain.sample(size=3)
   intel  sat
0      0    0
1      0    0
2      1    1

generate_sample(start_state=None, size=1, include_latents=False, seed=None)

Generator version of self.sample

Returns:

List of State namedtuples, representing the assignment to all variables of the model.

Examples

>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.sampling import GibbsSampling
>>> from pgmpy.models import DiscreteMarkovNetwork
>>> model = DiscreteMarkovNetwork([("A", "B"), ("C", "B")])
>>> factor_ab = DiscreteFactor(["A", "B"], [2, 2], [1, 2, 3, 4])
>>> factor_cb = DiscreteFactor(["C", "B"], [2, 2], [5, 6, 7, 8])
>>> model.add_factors(factor_ab, factor_cb)
>>> gibbs = GibbsSampling(model)
>>> gen = gibbs.generate_sample(size=2)
>>> [sample for sample in gen]
[[State(var='C', state=1), State(var='B', state=1), State(var='A', state=0)],
 [State(var='C', state=0), State(var='B', state=1), State(var='A', state=1)]]

sample(start_state=None, size=1, seed=None, include_latents=False)

Sample from the Markov Chain.

Parameters:

start_state: dict or array-like iterable

Representing the starting states of the variables. If None is passed, a random start_state is chosen.

size: int

Number of samples to be generated.

seed: int (default: None)

If a value is provided, sets the seed for numpy.random.

include_latents: boolean

Whether to include the latent variable values in the generated samples.

Returns:

sampled: pandas.DataFrame

The generated samples

Examples

>>> from pgmpy.factors.discrete import DiscreteFactor
>>> from pgmpy.sampling import GibbsSampling
>>> from pgmpy.models import DiscreteMarkovNetwork
>>> model = DiscreteMarkovNetwork([("A", "B"), ("C", "B")])
>>> factor_ab = DiscreteFactor(["A", "B"], [2, 2], [1, 2, 3, 4])
>>> factor_cb = DiscreteFactor(["C", "B"], [2, 2], [5, 6, 7, 8])
>>> model.add_factors(factor_ab, factor_cb)
>>> gibbs = GibbsSampling(model)
>>> gibbs.sample(size=4, return_tupe="dataframe")
   A  B  C
0  0  1  1
1  1  0  0
2  1  1  0
3  1  1  1