thomaspinder/GPJax
Gaussian processes in JAX and Flax.
This project helps machine learning researchers build and experiment with Gaussian process models for various tasks. It takes raw data and provides probabilistic predictions, uncertainty estimates, and model parameters for complex systems. It's ideal for those working on advanced statistical modeling and machine learning applications.
601 stars. Actively maintained with 5 commits in the last 30 days. Available on PyPI.
Use this if you are a researcher or advanced practitioner who needs flexibility and control to build custom Gaussian process models, conduct classifications, regressions, or Bayesian optimizations, and integrate with advanced JAX-based computations.
Not ideal if you are looking for a simple, out-of-the-box machine learning tool with a graphical interface or high-level API for quick deployment without deep customization.
Stars
601
Forks
71
Language
Python
License
MIT
Category
Last pushed
Mar 05, 2026
Commits (30d)
5
Dependencies
10
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