Approximate Inference Using Sampling

class pgmpy.inference.ApproxInference.ApproxInference(model)[source]
get_distribution(samples, variables, joint=True)[source]

Computes distribution of variables from given data samples.

Parameters
  • samples (pandas.DataFrame) – A dataframe of samples generated from the model.

  • variables (list (array-like)) – A list of variables whose distribution needs to be computed.

  • joint (boolean) – If joint=True, computes the joint distribution over variables. Else, returns a dict with marginal distribution of each variable in variables.

query(variables, n_samples=10000, evidence=None, virtual_evidence=None, joint=True, show_progress=True)[source]

Method for doing approximate inference based on sampling in Bayesian Networks and Dynamic Bayesian Networks.

Parameters
  • variables (list) – List of variables for which the probability distribution needs to be calculated.

  • n_samples (int) – The number of samples to generate for computing the distributions. Higher n_samples results in more accurate results at the cost of more computation time.

  • evidence (dict (default: None)) – The observed values. A dict key, value pair of the form {var: state_name}.

  • virtual_evidence (list (default: None)) – A list of pgmpy.factors.discrete.TabularCPD representing the virtual/soft evidence.

  • show_progress (boolean (default: True)) – If True, shows a progress bar when generating samples.

Returns

Probability distribution

Return type

An instance of pgmpy.factors.discrete.TabularCPD

Examples

>>> from pgmpy.utils import get_example_model
>>> from pgmpy.inference import ApproxInference
>>> model = get_example_model("alarm")
>>> infer = ApproxInference(model)
>>> infer.query(variables=["HISTORY"])
<DiscreteFactor representing phi(HISTORY:2) at 0x7f92d9f5b910>
>>> infer.query(variables=["HISTORY", "CVP"], joint=True)
<DiscreteFactor representing phi(HISTORY:2, CVP:3) at 0x7f92d9f77610>
>>> infer.query(variables=["HISTORY", "CVP"], joint=False)
{'HISTORY': <DiscreteFactor representing phi(HISTORY:2) at 0x7f92dc61eb50>,
 'CVP': <DiscreteFactor representing phi(CVP:3) at 0x7f92d915ec40>}