Tutorial: Gaussian Processes¶

The GP tutorial suite lives under notebooks/gp/ and covers exact, sparse, classification, heteroscedastic, multitask, spectral, and deep-kernel models.

Notebook Index¶

• GP_Exact_Tutorial.ipynb
• GP_Sparse_Tutorial.ipynb
• GP_Kernel_Zoo_Tutorial.ipynb
• GP_Classification_Tutorial.ipynb
• GP_Heteroscedastic_Tutorial.ipynb
• GP_MultiTask_ICM_Tutorial.ipynb
• GP_SpectralMixture_Tutorial.ipynb
• GP_DeepKernel_Tutorial.ipynb
• GP_Model_Comparison.ipynb

Legacy Paths¶

The original root notebook paths are kept as lightweight compatibility stubs:

• notebooks/GaussianProcess_Tutorial.ipynb
• notebooks/SparseGaussianProcess_Tutorial.ipynb

Common Evaluation Protocol¶

Across notebooks, we emphasize calibration-first metrics:

• RMSE
• Gaussian NLL
• 95% interval coverage
• Mean 95% interval width

Each notebook uses deterministic seeds and quick default configs for CPU-friendly runtime.