GPax: Gaussian Processes for Experimental Sciences
GPax is a small Python package for physics-based Gaussian processes (GPs) built on top of NumPyro and JAX. Its purpose is to take advantage of prior physical knowledge and different data modalities when using GPs for data reconstruction and active learning. It is a work in progress, and more models will be added in the near future.
Notes
Package Content
- GPax models
- Gaussian Processes - Fully Bayesian Implementation
- Gaussian Processes - Approximate Bayesian
- Deep Kernel Learning - Fully Bayesian Implementation
- Deep Kernel Learning - Approximate Bayesian
- Infinite-width Bayesian Neural Networks
- Multi-Task Learning
- Structured Probabilistic Models
- Acquisition functions
- Kernels
- Priors
- Hypothesis learning
- Utilities
Examples