Abstract: Bayesian inference is a powerful approach for integrating independent conflicting information for decision-making. Though an important component of robotic, biological, and other ...

In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a ...

Easily build Bayesian models from parts, abstract away the boilerplate, and tweak priors as you wish. Inspiration from Keras and Tensorflow Probability, but made specifically for Numpyro + Jax.

The PyRenew package is a flexible tool for simulation and statistical inference of epidemiological models, emphasizing hierarchical multi-signal renewal models. Built on top of the numpyro Python ...

In probabilistic programming, developers often face the challenge of efficiently composing and performing inference on intricate probabilistic programs. A recent release, Coix (COmbinators In jaX), ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...