MMM.sample_posterior_predictive

MMM.sample_posterior_predictive(X_pred, extend_idata=True, combined=True, include_last_observations=False, original_scale=True, **sample_posterior_predictive_kwargs)

Sample from the model’s posterior predictive distribution.

Parameters

X_predarray, shape (n_pred, n_features)

The input data used for prediction.

extend_idataBoolean determining whether the predictions should be added to inference data object.

Defaults to True.

combined: Combine chain and draw dims into sample. Won’t work if a dim named sample already exists.

Defaults to True.

include_last_observations: Boolean determining whether to include the last observations of the training

data in order to carry over costs with the adstock transformation. Assumes that X_pred are the next predictions following the training data. Defaults to False.

original_scale: Boolean determining whether to return the predictions in the original scale

of the target variable. Defaults to True.

**sample_posterior_predictive_kwargs: Additional arguments to pass to pymc.sample_posterior_predictive

Returns:

posterior_predictive_samplesDataArray, shape (n_pred, samples)

Posterior predictive samples for each input X_pred