MMM.sample_posterior_predictive

MMM.sample_posterior_predictive(X=None, 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:

Xarray, shape (n_pred, n_features)

The input data used for prediction.

extend_idatabool, optional

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

combined: bool, optional

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

include_last_observations: bool, optional

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 are the next predictions following the training data.Defaults to False.

original_scale: bool, optional

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