uncertainty

Gaussian Processes give predictions with principled uncertainty, which is why they power Bayesian optimization. What matters is the distribution-over-functions intuition, the kernel's role, and naming the O(n cubed) wall. Here is the answer.

Wrap any model and obtain prediction sets with a provable coverage rate, with no distributional assumptions needed. What shows depth is the calibration-set plus nonconformity-score mechanism and exactly what the guarantee does and does not promise. Here is the answer.

Models return confident answers on inputs unlike anything in their training set, which is how silent production failures start. The signal is knowing why raw softmax confidence misleads and which detectors genuinely separate in- from out-of-distribution.