BayesFlow: Amortized Bayesian Workflows With NeuralNetworks
Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul‐Christian Bürkner
Abstract
drawing conclusions from probabilistic models as part of principled workflows for data analysis (Bürkner et al., 2022;Gelman et al., 2020;Schad et al., 2021).Typical problems in Bayesian workflows are the approximation of intractable posterior distributions for diverse model types and the comparison of competing models of the same process in terms of their complexity and predictive performance.However, despite their theoretical appeal and utility, the practical execution of Bayesian workflows is often limited by computational bottlenecks: Obtaining even a single posterior may already take a long time, such that repeated estimation for the purpose of model validation or calibration becomes completely infeasible.