BayesFlow: Learning complex stochastic models with invertible neural networks

Understand

Estimating the parameters of mathematical models is a common problem in almost all branches of science.

• However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood function is not available.
• With this work, we propose a novel method for globally amortized Bayesian inference based on invertible neural networks which we call BayesFlow.
• The method uses simulation to learn a global estimator for the probabilistic mapping from observed data to underlying model parameters.