- dataset_dict : dict[str] -> InferenceData
A dictionary of model names and InferenceData objects
- ic : str
Information Criterion (WAIC or LOO) used to compare models. Default WAIC.
- method : str
Method used to estimate the weights for each model. Available options are:
- ‘stacking’ : (default) stacking of predictive distributions.
- ‘BB-pseudo-BMA’ : pseudo-Bayesian Model averaging using Akaike-type
- weighting. The weights are stabilized using the Bayesian bootstrap
- ‘pseudo-BMA’: pseudo-Bayesian Model averaging using Akaike-type
- weighting, without Bootstrap stabilization (not recommended)
For more information read https://arxiv.org/abs/1704.02030
- b_samples: int
Number of samples taken by the Bayesian bootstrap estimation.
Only useful when method = ‘BB-pseudo-BMA’.
- alpha : float
The shape parameter in the Dirichlet distribution used for the Bayesian bootstrap. Only
useful when method = ‘BB-pseudo-BMA’. When alpha=1 (default), the distribution is uniform
on the simplex. A smaller alpha will keeps the final weights more away from 0 and 1.
- seed : int or np.random.RandomState instance
If int or RandomState, use it for seeding Bayesian bootstrap. Only
useful when method = ‘BB-pseudo-BMA’. Default None the global
np.random state is used.
- scale : str
Output scale for IC. Available options are:
- deviance : (default) -2 * (log-score)
- log : 1 * log-score (after Vehtari et al. (2017))
- negative_log : -1 * (log-score)