pymc.sample_prior_predictive#

pymc.sample_prior_predictive(samples=500, model=None, var_names=None, random_seed=None, return_inferencedata=True, idata_kwargs=None, compile_kwargs=None)[source]#

Generate samples from the prior predictive distribution.

Parameters
samplesint

Number of samples from the prior predictive to generate. Defaults to 500.

modelModel (optional if in with context)
var_namesIterable[str]

A list of names of variables for which to compute the prior predictive samples. Defaults to both observed and unobserved RVs. Transformed values are not included unless explicitly defined in var_names.

random_seedint, RandomState or Generator, optional

Seed for the random number generator.

return_inferencedatabool

Whether to return an arviz.InferenceData (True) object or a dictionary (False). Defaults to True.

idata_kwargsdict, optional

Keyword arguments for pymc.to_inference_data()

compile_kwargs: dict, optional

Keyword arguments for pymc.aesaraf.compile_pymc().

Returns
arviz.InferenceData or Dict

An ArviZ InferenceData object containing the prior and prior predictive samples (default), or a dictionary with variable names as keys and samples as numpy arrays.