sbijax 0.2.0

Simulation-based inference in JAX

sbijax

Simulation-based inference in JAX

About

sbijax implements several algorithms for simulation-based inference in JAX using Haiku, Distrax and BlackJAX. Specifically, sbijax implements

• Sequential Monte Carlo ABC (SMCABC)
• Neural Likelihood Estimation (SNL)
• Surjective Neural Likelihood Estimation (SSNL)
• Neural Posterior Estimation C (short SNP)
• Contrastive Neural Ratio Estimation (short SNR)
• Neural Approximate Sufficient Statistics (SNASS)
• Neural Approximate Slice Sufficient Statistics (SNASSS)
• Flow matching posterior estimation (SFMPE)
• Consistency model posterior estimation (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.

Documentation

Documentation can be found here.

Installation

Make sure to have a working JAX installation. Depending whether you want to use CPU/GPU/TPU, please follow these instructions.

To install from PyPI, just call the following on the command line:

pip install sbijax

To install the latest GitHub , use:

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 package which is substantially more feature-complete and user-friendly, and better documented.

Author

Simon Dirmeier sfyrbnd @ pm me