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:
- samples
int Number of samples from the prior predictive to generate. Defaults to 500.
- model
Model(optionalifinwithcontext) - var_names
Iterable[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 allowed.
- random_seed
int,RandomStateorGenerator, 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_kwargs
dict, optional Keyword arguments for
pymc.to_inference_data()- compile_kwargs: dict, optional
Keyword arguments for
pymc.pytensorf.compile_pymc().
- samples
- Returns:
arviz.InferenceDataorDictAn ArviZ
InferenceDataobject containing the prior and prior predictive samples (default), or a dictionary with variable names as keys and samples as numpy arrays.