# sbijax
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> Simulation-based inference in JAX
## About
`sbijax` implements several algorithms for simulation-based inference in
[JAX](https://github.com/google/jax) using [Haiku](https://github.com/deepmind/dm-haiku),
[Distrax](https://github.com/deepmind/distrax) and [BlackJAX](https://github.com/blackjax-devs/blackjax). Specifically, `sbijax` implements
- [Sequential Monte Carlo ABC](https://www.routledge.com/Handbook-of-Approximate-Bayesian-Computation/Sisson-Fan-Beaumont/p/book/9780367733728) (`SMCABC`)
- [Neural Likelihood Estimation](https://arxiv.org/abs/1805.07226) (`SNL`)
- [Surjective Neural Likelihood Estimation](https://arxiv.org/abs/2308.01054) (`SSNL`)
- [Neural Posterior Estimation C](https://arxiv.org/abs/1905.07488) (short `SNP`)
- [Contrastive Neural Ratio Estimation](https://arxiv.org/abs/2210.06170) (short `SNR`)
- [Neural Approximate Sufficient Statistics](https://arxiv.org/abs/2010.10079) (`SNASS`)
- [Neural Approximate Slice Sufficient Statistics](https://openreview.net/forum?id=jjzJ768iV1) (`SNASSS`)
- [Flow matching posterior estimation](https://arxiv.org/abs/2305.17161) (`SFMPE`)
- [Consistency model posterior estimation](https://arxiv.org/abs/2312.05440) (`SCMPE`)
where the acronyms in parentheses denote the names of the methods in `sbijax`.
> [!CAUTION]
> ⚠️ As per the LICENSE file, there is no warranty whatsoever for this free software tool. If you discover bugs, please report them.
## Examples
You can find several self-contained examples on how to use the algorithms in [examples](https://github.com/dirmeier/sbijax/tree/main/examples).
## Documentation
Documentation can be found [here](https://sbijax.readthedocs.io/en/latest/).
## Installation
Make sure to have a working `JAX` installation. Depending whether you want to use CPU/GPU/TPU,
please follow [these instructions](https://github.com/google/jax#installation).
To install from PyPI, just call the following on the command line:
```bash
pip install sbijax
```
To install the latest GitHub <RELEASE>, use:
```bash
pip install git+https://github.com/dirmeier/sbijax@<RELEASE>
```
## Acknowledgements
> [!NOTE]
> 📝 The API of the package is heavily inspired by the excellent Pytorch-based [`sbi`](https://github.com/sbi-dev/sbi) package which is substantially more
feature-complete and user-friendly, and better documented.
## Author
Simon Dirmeier <a href="mailto:sfyrbnd @ pm me">sfyrbnd @ pm me</a>
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