DelayedSaturatedMMM.sample_posterior_predictive#
- DelayedSaturatedMMM.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_samples
DataArray,shape(n_pred,samples) Posterior predictive samples for each input X_pred
- posterior_predictive_samples