[!IMPORTANT] š£ As of April 2024 I recommends that new projects should adopt NumPyro instead of Jaxampler. š£
At the time of writing NumPyro has a larger and more active development team and more adoption with users. It has more extensive documentation, examples and an active community creating end to end examples.
Jaxampler will be a public archive but I have no plans to take it down from PyPI public repositories.
Jaxampler š§Ŗ is a statistical sampling toolkit built on top of JAX. It provides a set of high-performance sampling algorithms for a wide range of statistical distributions. Jaxampler is designed to be easy to use and integrate with existing JAX workflows. It is also designed to be extensible, allowing users to easily add new sampling algorithms and statistical distributions.
Jaxampler is currently in the early stages of development and is not yet ready for production use. However, we welcome contributions from the community to help us improve the library. If you are interested in contributing, please refer to our contribution guidelines.
You may install the latest released version of Jaxampler through pip by doing
pip3 install --upgrade jaxampler
You may install the bleeding edge version by cloning this repo or doing
pip3 install --upgrade git+https://github.com/Qazalbash/jaxampler
If you would like to take advantage of CUDA, you will additionally need to install a specific version of JAX by doing
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
Jaxampler requires Python 3.10 or later. It also requires the following packages:
jax>=0.4.0
jaxlib>=0.4.0
typing_extensions>=4.5.0
jaxtyping>=0.2.24
matplotlib>=3.8.0
tfp-nightly
tqdm
The test suite is based on pytest
. To run the tests, one needs to install pytest and run pytest
at the root
directory of this repo.
Jaxampler currently supports the following algorithms and distributions:
- [x] Accept-Rejection Sampler - [x] Adaptive Accept-Rejection Sampler - [ ] Gibbs Sampler - [x] Hastings Sampler - [x] Inverse Transform Sampler - [x] Metropolis-Hastings Sampler - [ ] Slice Sampler
- [x] Bernoulli - [x] Beta - [x] Binomial - [x] Boltzmann - [x] Cauchy - [x] Chi - [x] Exponential - [x] Gamma - [x] Geometric - [ ] Gumbel - [ ] Laplace - [x] Log Normal - [x] Logistic - [ ] Multivariate Normal - [x] Normal - [x] Pareto - [x] Poisson - [ ] Rademacher - [x] Rayleigh - [x] Student t - [x] Triangular - [x] Truncated Normal - [x] Truncated Power Law - [x] Uniform - [x] Weibull
To cite this repository:
@software{jaxampler2023github,
author = {Meesum Qazalbash},
title = {{Jaxampler}: tool for sampling statistical distributions},
url = {https://github.com/Qazalbash/jaxampler},
version = {0.0.7},
year = {2023}
}