BaseValidateMMM.sample_prior_predictive

BaseValidateMMM.sample_prior_predictive(X_pred, y_pred=None, samples=None, extend_idata=True, combined=True, **kwargs)

Sample from the model’s prior predictive distribution.

Parameters:

X_predarray, shape (n_pred, n_features)

The input data used for prediction using prior distribution.

samplesint

Number of samples from the prior parameter distributions to generate. If not set, uses sampler_config[‘draws’] if that is available, otherwise defaults to 500.

extend_idataBoolean

Determine whether the predictions should be added to inference data object. Defaults to True.

combined: Boolean

Combine chain and draw dims into sample. Won’t work if a dim named sample already exists. Defaults to True.

**kwargs: Additional arguments to pass to pymc.sample_prior_predictive

Returns:

prior_predictive_samplesDataArray, shape (n_pred, samples)

Prior predictive samples for each input X_pred