[2602.07098] BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python

Statistics > Computation arXiv:2602.07098 (stat) [Submitted on 6 Feb 2026 (v1), last revised 23 Mar 2026 (this version, v2)] Title:BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python Authors:Lars Kühmichel, Jerry M. Huang, Valentin Pratz, Jonas Arruda, Hans Olischläger, Daniel Habermann, Simon Kucharsky, Lasse Elsemüller, Aayush Mishra, Niels Bracher, Svenja Jedhoff, Marvin Schmitt, Paul-Christian Bürkner, Stefan T. Radev View a PDF of the paper titled BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python, by Lars K\"uhmichel and 13 other authors View PDF Abstract:Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows. An overarching motif of many Bayesian methods is that they are relatively slow, which often becomes prohibitive when fitting complex models to large data sets. Amortized Bayesian inference (ABI) offers a path to solving the computational challenges of Bayes. ABI trains neural networks on model simulations, rewarding users with rapid inference of any model-implied quantity, such as point estimates, likelihoods, or full posterior distributions. In this work, we present the Python library BayesFlow, Version 2.0, for general-purpose ABI. Along with direct posterior, likelihood, and ratio estimation, the software includes support for multiple popular deep learning backends, a rich collection of generative networks ...