estimators

A collection of Estimators, which embody the inference task and the neural network components used to perform it.

Classes

AutoregressiveEstimator(*args, **kwargs)	Estimate a joint smoothing or filtering distribution with an arbitrary inference network.
BasicEstimator(*args, **kwargs)	Defines a wrapper for estimating arbitrary continuous distributions of the form: p(inference_variables | summary(summary_variables), inference_conditions)
CompositionalEstimator(*args, **kwargs)	Defines a wrapper for estimating arbitrary compositional distributions of the form: p(inference_variables | [summary(summary_variables), ...], inference_conditions)
EnsembleEstimator(*args, **kwargs)	Combines multiple estimators into a single ensemble.
Estimator(*args, **kwargs)	Base class for all BayesFlow estimators.
ModelComparisonEstimator(*args, **kwargs)	Defines an estimator for model (simulator) comparison, where the (discrete) posterior model probabilities are learned with a classifier implementing multi- class, stabilized versions of all losses described in [1].
RatioEstimator(*args, **kwargs)	Implements contrastive neural likelihood-to-evidence ratio estimation (NRE-C) as described in https://arxiv.org/pdf/2210.06170.
ScoringRuleEstimator(*args, **kwargs)	A workflow for fast amortized Bayes risk minimization for arbitrary scoring rules.