This page moves from prior beliefs to conditional inference. Add observations, change the kernel, and inspect how uncertainty behaves away from the training points.
The demo is intentionally small and one-dimensional so that the mechanics remain visible: kernel choice, data placement, and noise level all leave recognizable signatures in the posterior.
Concept: Conditioning, uncertainty shrinkage, and observation-noise effects in one-dimensional Gaussian-process regression.