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.

Likelihood-free AMortized Posterior Estimation with PyTorch

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.

Simulation-based inference in JAX

(NeurIPS 2022) Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

Fast, lightweight and parallelised simulation-based inference in JAX.

Conduct simulation-based inference on strong gravitational lensing systems.

A toolbox for the calibration and evaluation of simulation models.

A simulation-based Inference (SBI) library designed to perform analysis on a wide class of gravitational wave signals

Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

Code for reproducing the experiments in the paper Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference.

A simulation model for the digital reconstruction of 3D root system architectures. Integrated with a simulation-based inference generative deep learning model.

Created for benchmarking different techniques to search for CP violation in the HWW vertex in leptonic WH production.

Simulation-based inference using SSNL

Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference

pyLFI is a Python toolbox using likelihood-free inference (LFI) methods for estimating the posterior distributions of model parameters.

Gaussian Mixture Model likelihood with support for heterogeneous missing and censored (upper limit) data.

High-fidelity artificial NoN (Network of Networks) generator