GPJax
A low-level Gaussian process framework in JAX and Flax, designed for maximum flexibility and close alignment with mathematical notation.
What is GPJax?
GPJax is a Gaussian process framework built in JAX and Flax that provides a low-level interface for probabilistic modeling. It enables flexible construction, inference, and customization of Gaussian process models, closely aligning code with mathematical notation. The library supports various inference methods, custom kernels, and applications like regression, classification, and Bayesian optimization.
Researchers and machine learning practitioners who need a flexible, extensible Gaussian process library for advanced modeling, experimentation, and custom algorithm development within the JAX ecosystem.
Developers choose GPJax for its low-level, mathematically transparent interface that offers maximum flexibility for extending GP models, combined with the performance benefits of JAX's automatic differentiation and compilation.
Overview
Gaussian processes in JAX and Equinox.
Use Cases
Best For
- Research requiring custom Gaussian process model extensions
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