Open-source alternatives to Dynamax

This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for performing statistical inference and quantifying uncertainty through a probabilistic programming workflow using Python. The resource employs a computation-first pedagogy, teaching Bayesian methods and parameter estimation through executable code and simulations instead of formal mathematical notation. It provides a practical approach to implementing Markov Chain Monte Carlo sampling to estimate posterior distributions. The content covers building probabilistic models, integrating e

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Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

MCX

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis integrated with the TensorFlow ecosystem. It serves as a Bayesian deep learning framework, a probabilistic programming interface, and a variational inference engine, providing a toolset for Markov chain Monte Carlo sampling and tensor-based probabilistic modeling. The project enables the construction of neural networks with probabilistic weights and the implementation of Bayesian neural networks to quantify prediction uncertainty. It provides specialized capabilities for hierarchical probabilistic modelin

Gaussian processes in Jax. -->

Approximate inference for Markov (i.e., temporal) Gaussian processes using iterated Kalman filtering and smoothing. Developed and maintained by William Wilkinson. The Bernoulli likelihood was implemented by Paul Chang. We are based in Arno Solin's machine learning group at Aalto University, Finland.

PGMax implements general factor graphs for discrete probabilistic graphical models (PGMs), and hardware-accelerated differentiable loopy belief propagation (LBP) in JAX.

Fortuna #######

BlackJAX is a library of samplers for JAX that works on CPU as well as GPU.

the tiniest of Gaussian Process libraries