Simulation-based inference in JAX
Project description
sbijax
Simulation-based inference in JAX
About
SbiJAX implements several algorithms for simulation-based inference using JAX, Haiku and BlackJAX.
SbiJAX so far implements
- Rejection ABC (
RejectionABC), - Sequential Monte Carlo ABC (
SMCABC), - Sequential Neural Likelihood Estimation (
SNL) - Surjective Sequential Neural Likelihood Estimation (
SSNL) - Sequential Neural Posterior Estimation C (short
SNP)
Examples
You can find several self-contained examples on how to use the algorithms in examples.
Usage
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>
Author
Simon Dirmeier sfyrbnd @ pm me
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sbijax-0.1.0.tar.gz
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sbijax-0.1.0-py3-none-any.whl
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