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_pred (array, shape (n_pred, n_features)) – The input data used for prediction.

• extend_idata (Boolean 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 – Posterior predictive samples for each input X_pred

Return type:

DataArray, shape (n_pred, samples)