BaseMMM.sample_prior_predictive#
- BaseMMM.sample_prior_predictive(X=None, y=None, samples=None, extend_idata=True, combined=True, **kwargs)#
Sample from the model’s prior predictive distribution.
- Parameters:
- X
array,shape(n_pred,n_features) The input data used for prediction using prior distribution.
- y
array,shape(n_pred,), optional The target values (real numbers) used for prediction using prior distribution. If not set, defaults to an array of zeros.
- samples
int 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_idata
Boolean 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
- X
- Returns:
- prior_predictive_samples
DataArray,shape(n_pred,samples) Prior predictive samples for each input X
- prior_predictive_samples