sparse-tinygp

Bayesian inference using sparse gaussian processes via tinygp. Examples include 1D and 2D implementation.

Notebooks

1. 01_inference_sparse_gp.ipynb: SVI with a Sparse GP
2. 02_2d_sparse_gp.ipynb: 2D Sparse GP
3. 03_rffs_sparse_gp.ipynb: SVI with RFF-approximation to sparse-GP (Sparse GP helps fitting, RFF helps sampling)

Installation

Run the environment.yml file by running the following command on the main repo directory:

conda env create

The installation works for conda==4.12.0. This will install all packages needed to run the code on a CPU with jupyter.

If you want to run this code with a CUDA GPU, you will need to download the appropriate jaxlib==0.4.13 version. For example, for my GPU running on CUDA==12.3, I would run:

pip install jaxlib==0.4.13+cuda12.cudnn89

The key to using this code directly would be to retain the jax and jaxlib versions.