sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.
A Python library for amortized Bayesian workflows using generative neural networks.
A system for scientific simulation-based inference at scale.
Community-sourced list of papers and resources on neural simulation-based inference.
Likelihood-free AMortized Posterior Estimation with PyTorch
R package for statistical inference using partially observed Markov processes
Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead
A Python toolkit for (simulation-based) inference and the mechanization of science.
Automatically convert Julia methods to Gen functions.
Simulation-based inference in JAX
Simulation-based (likelihood-free) inference customized for astronomical applications
Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration
Julia package for neural estimation
SBI Workshop jointly by Helmholtz AI + ML ⇌ Science Colaboratory
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., AISTATS 2025)
ML4FP 2025: notebooks used for the Machine Learning for Fundamental Physics (ML4FP) School 2025
Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.
(NeurIPS 2022) Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Fast, lightweight and parallelised simulation-based inference in JAX.
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