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.

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

Notebook for implementing Monte Carlo techniques (Metropolis-Hastings and Augmented Gibbs) to solve a Bayesian Probit regression. This Jupyter Notebook implements Bayesian modeling techniques to fit a ...

Bayesian inference is a method of statistical inference that uses Bayes’ Theorem to update the probability of a hypothesis as new evidence or data becomes available. It combines prior knowledge with ...

Abstract: Probabilistic programming languages (PPLs) are at the interface between statistics and the theory of programming languages. PPLs formulate statistical models as stochastic programs that ...

If you are interested in learning more about machine learning inference on the recently launched Raspberry Pi Pico microcontroller, you may be interested in a new project published to the Hackster.io ...

This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely ...