Scientists use numerical simulators to model real-world phenomena. Unlike purely statistical models, simulators incorporate scientific principles, which help them make better predictions with fewer parameters. However, fine-tuning these parameters to match real-world data is difficult. Simulation-Based inference (SBI) aims to find parameter values that align with prior knowledge and observed data. In other words, SBI aims to perform fully Bayesian parameter inference on simulation-based models, i.e., using only simulated data and without requiring access to the likelihood function.

SBI is an active field of research with new algorithms being developed regularly. The
`sbi`

package aims to provide a central resource for practitioners and researchers to
gain access to state-of-the-art SBI algorithms, as well as extensive documentation and
tutorials.

`pip install sbi`

, or check out the documentation## Inference methods

- Neural posterior estimation (amortized and sequential [Pap18F, Lue17F, Gre19A])
- Neural likelihood estimation [Pap19S]
- Neural ratio estimation [Her20L]
- Balanced neural ratio estimation [Del22R]
- Neural variational inference (amortized and sequential [Glo22V])
- Mixed neural likelihood estimation [Boe22F]
- Parallelized MCMC slice sampling
- Access to Pyro and PyMC MCMC samplers

## Evaluation methods

- Extensive plotting tools
- Prior and posterior predictive checks
- Access to arviz visualizations
- Simulation-based calibration [Tal20V]
- Classifier-based two-sample test (C2ST, [Lop17R])
- Expected coverage [Dei22T, Mil21T]

## Roadmap

We are currently implementing or plan to implement:

- Flow-matching neural posterior estimation [Lip22F, Wil23F]
- Sequential neural posterior score estimation [Sha22S]
- Compositional neural score estimation [Gef23C]
- Local-C2ST [Lin23L]

## Acknowledgments

The `sbi`

package originated at the **University of Tübingen** in the
mackelab, funded 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) (see
here for details).

Today, `sbi`

is a community project maintained by a group of people from both the
University of Tübingen and the TransferLab. It has a large group of contributors from
across Europe—new contributors are
welcome.