probly.method

Transformations for models.

Submodules

batchensemble(base[, num_members, ...])	Create a BatchEnsemble predictor from a base predictor based on [WTB20].
bayesian(base[, use_base_weights, ...])	Create a Bayesian predictor from a base predictor based on [BCKW15].
calibration	Calibration transformations for logit predictors.
cast(base)	Return a predictor unchanged while optionally registering its predictor type.
conformal	Conformal transformations for regression and classification.
credal_bnn	CredalBNN: Credal Bayesian Neural Networks for Uncertainty Estimation in Deep Learning.
credal_ensembling	Credal ensembling method.
credal_net	Credal net implementation for uncertainty quantification.
credal_relative_likelihood	Credal relative likelihood method.
credal_wrapper(base, num_members[, reset_params])	Create a credal wrapper predictor from a base predictor based on [WCS+24].
dare	DARE implementation for uncertainty quantification.
ddu(base[, sn_coeff])	Transform a model for Deep Deterministic Uncertainty based on [MKvA+23].
dropconnect(base[, p, rng_collection, rngs])	Create a DropConnect predictor from a base predictor based on [MNM+19].
dropout(base[, p, rng_collection, rngs])	Create a Dropout predictor from a base predictor based on [GG16a].
duq(base[, centroid_size, length_scale, gamma])	Transform a model for Deterministic Uncertainty Quantification [vASTG20].
efficient_credal_prediction(base)	Create an efficient credal predictor from a base predictor based on [HLohrM+26].
ensemble(base, num_members[, reset_params])	Create an ensemble predictor from a base predictor based on [LPB17a].
evidential	Evidential module for probly.
het_nets	HetNets implementation for uncertainty quantification.
method	Utilities for the definition of methods.
natural_posterior_network	Module for natural posterior network implementations.
posterior_network	Module for posterior network implementations.
prior_network	Module for Prior Network implementation.
sngp(base[, name, n_power_iterations, ...])
subensemble(base, num_heads[, head, ...])	Create a subensemble predictor from a base model or a base model and head model.