BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python

TL;DR

BayesFlow 2.0 is a versatile Python library that enables fast, amortized Bayesian inference across multiple deep learning backends, supporting complex models and large datasets with new features for optimization and hierarchical modeling.

Contribution

The paper introduces BayesFlow 2.0, a comprehensive Python library that extends amortized Bayesian inference with support for multiple backends, advanced features, and improved usability.

Findings

Demonstrates rapid inference in a dynamical system case study.

Shows strong potential for broad adoption in Bayesian modeling workflows.

Provides comparative analysis with similar software tools.

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 for sampling and density estimation, complete customization and high-level interfaces, as well as new capabilities for hyperparameter optimization, design optimization, and hierarchical modeling. Using a case study on dynamical system parameter estimation, combined with comparisons to similar software, we show that our streamlined, user-friendly workflow has strong potential to support broad adoption.