1. Introduction -- 2. Bayesian nonlinear regression with Gaussian process priors -- 3. Inference and computation for Gaussian process regression (GPR) model -- 4. Covariance function and model selection -- 5. Functional regression analysis -- 6. Mixture models and curve clustering -- 7. Generalized Gaussian process regression for non-Gaussian functional data -- 8. Some other related models.