Tutorials

Introductory

• Basic regression using the GPR model
• Choosing and combining kernels

Approximate inference

• Basic classification using the VGP model
• Basic classification using the PEP model
• Basic classification using the CVIGaussianProcess model
• Basic classification using the SPEP model
• Basic classification using the SparseCVIGaussianProcess model
• Classification using importance-weighted SGPR

Special kernels and likelihoods

• Factor Analysis
• Stacked kernels and multiple outputs
• Regression using a piecewise kernel
• Demo of MultiStageLikelihood with plain SVGP model