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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.

bayesian-inferencelikelihood-free-inferencemachine-learningparameter-estimationpytorchsimulation-based-inference

PyPI version
Conda Version
Contributions welcome
Tests
codecov
GitHub license
DOI
NumFOCUS affiliated

sbi: Simulation-Based Inference

Getting Started |
Documentation | Discord Server

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.

With sbi, you can perform parameter inference using Bayesian inference: Given a
simulator that models a real-world process, SBI estimates the full posterior
distribution over the simulator’s parameters based on observed data. This distribution
indicates the most likely parameter values while additionally quantifying uncertainty
and revealing potential interactions between parameters.

Key Features of sbi

sbi offers a blend of flexibility and ease of use:

  • Low-Level Interfaces: For those who require maximum control over the inference
    process, sbi provides low-level interfaces that allow you to fine-tune many aspects
    of your workflow.
  • High-Level Interfaces: If you prefer simplicity and efficiency, sbi also offers
    high-level interfaces that enable quick and easy implementation of complex inference
    tasks.

In addition, sbi supports a wide range of state-of-the-art inference algorithms (see
below for a list of implemented methods):

  • Amortized Methods: These methods enable the reuse of posterior estimators across
    multiple observations without the need to retrain.
  • Sequential Methods: These methods focus on individual observations, optimizing the
    number of simulations required.

Beyond inference, sbi also provides:

  • Validation Tools: Built-in methods to validate and verify the accuracy of your
    inferred posteriors.
  • Plotting and Analysis Tools: Comprehensive functions for visualizing and analyzing
    results, helping you interpret the posterior distributions with ease.

Getting started with sbi is straightforward, requiring only a few lines of code:

from sbi.inference import NPE
# Given: parameters theta and corresponding simulations x
inference = NPE(prior=prior)
inference.append_simulations(theta, x).train()
posterior = inference.build_posterior()

Installation

sbi requires Python 3.10 or higher. While a GPU isn't necessary, it can improve
performance in some cases. We recommend using a virtual environment with
conda for an easy setup.

If conda is installed on the system, an environment for installing sbi can be created as follows:

conda create -n sbi_env python=3.10 && conda activate sbi_env

From PyPI

To install sbi from PyPI run

python -m pip install sbi

From conda-forge

To install and add sbi to a project with pixi, from the project directory run

pixi add sbi

and to install into a particular conda environment with conda, in the activated environment run

conda install --channel conda-forge sbi

If uv is installed on the system, an environment for installing sbi can be created as follows:

uv venv -p 3.10

Then activate the virtual enviroment by running:

  • For macOS or Linux users

    source .venv/bin/activate

  • For Windows users

    .venv\Scripts\activate

To install sbi run

uv add sbi

Testing the installation

Open a Python prompt and run

from sbi.examples.minimal import simple
posterior = simple()
print(posterior)

Tutorials

If you're new to sbi, we recommend starting with our Getting
Started tutorial.

You can also access and run these tutorials directly in your browser by opening
Codespace. To do so, click the green
“Code” button on the GitHub repository and select “Open with Codespaces.” This provides
a fully functional environment where you can explore sbi through Jupyter notebooks.

You might also find this tutorial paper useful: Deistler, M., Boelts, J., Steinbach, P., Moss, G., Moreau, T., Gloeckler, M., ... & Macke, J. H. (2025). Simulation-based inference: A practical guide. arXiv preprint arXiv:2508.12939.. It describes the SBI workflow and offers practical guidelines and diagnostic tools for every stage of the process: from setting up the simulator and prior, choosing and training inference networks, to performing inference and validating the results. It also includes several worked examples.

Inference Algorithms

The following inference algorithms are currently available. You can find instructions on
how to run each of these methods
here.

Neural Posterior Estimation: amortized (NPE) and sequential (SNPE)

Neural Likelihood Estimation: amortized (NLE) and sequential (SNLE)

Neural Ratio Estimation: amortized (NRE) and sequential (SNRE)

Neural Variational Inference, amortized (NVI) and sequential (SNVI)

Mixed Neural Likelihood Estimation (MNLE)

Feedback and Contributions

We welcome any feedback on how sbi is working for your inference problems (see
Discussions) and are happy to receive bug
reports, pull requests, and other feedback (see
contribute). We wish to maintain a
positive and respectful community; please read our Code of
Conduct.

Acknowledgments

sbi is the successor (using PyTorch) of the
delfi package. It started as a fork of Conor M.
Durkan's lfi. sbi runs as a community project. See also
credits.

Support

sbi has been supported by the German Federal Ministry of Education and Research (BMBF)
through project ADIMEM (FKZ 01IS18052 A-D), project SiMaLeSAM (FKZ 01IS21055A) and the
Tübingen AI Center (FKZ 01IS18039A). Since 2024, sbi is supported by the appliedAI
Institute for Europe, and by NumFOCUS.

License

Apache License Version 2.0 (Apache-2.0)

Citation

The sbi package has grown and improved significantly since its initial release, with
contributions from a large and diverse community. To reflect these developments and the
expanded functionality, we published an updated JOSS
paper. We encourage you to cite this
newer version as the primary reference:

@article{BoeltsDeistler_sbi_2025,
  doi = {10.21105/joss.07754},
  url = {https://doi.org/10.21105/joss.07754},
  year = {2025},
  publisher = {The Open Journal},
  volume = {10},
  number = {108},
  pages = {7754},
  author = {Jan Boelts and Michael Deistler and Manuel Gloeckler and Álvaro Tejero-Cantero and Jan-Matthis Lueckmann and Guy Moss and Peter Steinbach and Thomas Moreau and Fabio Muratore and Julia Linhart and Conor Durkan and Julius Vetter and Benjamin Kurt Miller and Maternus Herold and Abolfazl Ziaeemehr and Matthijs Pals and Theo Gruner and Sebastian Bischoff and Nastya Krouglova and Richard Gao and Janne K. Lappalainen and Bálint Mucsányi and Felix Pei and Auguste Schulz and Zinovia Stefanidi and Pedro Rodrigues and Cornelius Schröder and Faried Abu Zaid and Jonas Beck and Jaivardhan Kapoor and David S. Greenberg and Pedro J. Gonçalves and Jakob H. Macke},
  title = {sbi reloaded: a toolkit for simulation-based inference workflows},
  journal = {Journal of Open Source Software}
}

This updated paper, with its expanded author list, reflects the broader community
contributions and the package's enhanced capabilities in releases
0.23.0 and later.

If you are using a version of sbi prior to 0.23.0, please cite the original sbi
software paper:

@article{tejero-cantero2020sbi,
  doi = {10.21105/joss.02505},
  url = {https://doi.org/10.21105/joss.02505},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {52},
  pages = {2505},
  author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke},
  title = {sbi: A toolkit for simulation-based inference},
  journal = {Journal of Open Source Software}
}

Regardless of which software paper you cite, please also remember to cite the original
research articles describing the specific sbi-algorithm(s) you are using.

Specific releases of sbi are also citable via
Zenodo, where we generate a new software DOI for
each release.

Languages

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Apache License 2.0
Created February 11, 2020
Updated March 9, 2026
sbi-dev/sbi | GitHunt