[2409.19435] Simulation-based Inference with the Python Package sbijax

Computer Science > Machine Learning arXiv:2409.19435 (cs) [Submitted on 28 Sep 2024 (v1), last revised 20 Mar 2026 (this version, v2)] Title:Simulation-based Inference with the Python Package sbijax Authors:Simon Dirmeier, Antonietta Mira, Carlo Albert View a PDF of the paper titled Simulation-based Inference with the Python Package sbijax, by Simon Dirmeier and Antonietta Mira and Carlo Albert View PDF HTML (experimental) Abstract:Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate models. Here we introduce sbijax, a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based inference using a user-friendly programming interface. sbijax offers high-level functionality to quickly construct SBI estimators, and compute and visualize posterior distributions with only a few lines of code. In addition, the package provides functionality for conventional approximate Bayesian computation, to compute model diagnostics, and to automatically estimate summary statistics. By virtue of being entirely written in JAX, sbijax is extremely computationally efficient, allowing rapid training of neural networks and executing code automatically in parallel on both CPU and GPU. Subjects: Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML) Cite as: arXiv:2409.19435 [cs.LG]   (or arXiv:2409.19435v2 [cs.LG] f...