jaxampler 0.0.4

JAX based lib for sampling statistical distributions.

JAXampler 🧪 - A JAX-based statistical sampling toolkit

JAXampler 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.

Features

• 🚀 High-Performance Sampling: Leverage the power of JAX for high-speed, accurate sampling.
• 🧩 Versatile Algorithms: A wide range of sampling methods to suit various applications.
• 🔗 Easy Integration: Seamlessly integrates with existing JAX workflows.

Install

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

Requirements

JAXampler requires Python 3.10 or later. It also requires the following packages:

jax>=0.4.0
jaxlib>=0.4.0
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.

Samplers

To sample from a distribution, import the corresponding sampler from the jaxampler.sampler module and call the sample method with the required arguments.

from jaxampler.rvs import Beta, Normal
from jaxampler.sampler import AcceptRejectSampler

scale = 1.35
N = 100_000

target_rv = Normal(mu=0.5, sigma=0.2)
proposal_rv = Beta(alpha=2, beta=2)

ar_sampler = AcceptRejectSampler()

samples = ar_sampler.sample(
    target_rv=target_rv,
    proposal_rv=proposal_rv,
    scale=scale,
    N=N,
)

JAXampler currently supports the following samplers:

• Inverse Transform Sampler
• Accept-Rejection Sampler
• Adaptive Accept-Rejection Sampler
• Metropolis-Hastings Sampler
• Hamiltonian Monte Carlo Sampler
• Slice Sampler
• Gibbs Sampler
• Importance Sampler

Random Variables

To create a new random variable, import the corresponding type from the jaxampler.rvs i.e. DiscreteRV and ContinuousRV for discrete and continuous random variables respectively. Then, instantiate the random variable with the required parameters and implement the necessary methods (logpdf, cdf, and ppf etc). JAXampler currently supports the following random variables:

Discrete Random Variables

• Bernoulli
• Binomial
• Geometric
• Poisson
• Rademacher

Continuous Random Variables

• Beta
• Boltzmann
• Cauchy
• Chi
• Exponential
• Gamma
• Gumbel
• Laplace
• Log Normal
• Logistic
• Multivariate Normal
• Normal
• Pareto
• Rayleigh
• Student t
• Triangular
• Truncated Normal
• Truncated Power Law
• Uniform
• Weibull

Citing Jaxampler

To cite this repository:

@software{jaxampler2023github,
    author  = {Meesum Qazalbash},
    title   = {{JAXampler}: A JAX-based statistical sampling toolkit},
    url     = {http://github.com/Qazalbash/jaxampler},
    version = {0.0.4},
    year    = {2023}
}

Contributors

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