Gaussian Process Regression

A nonparametric Bayesian regression method that models an unknown function as a Gaussian process: a collection of random variables indexed by input points with joint Gaussian distributions specified by a mean function and covariance (kernel) function.

A nonparametric Bayesian regression method that places a Gaussian process prior over functions, specifying a mean function and a covariance (kernel) that encodes similarity; conditioned on observed input–output pairs it yields a Gaussian posterior predictive distribution at new inputs, providing mean predictions and principled uncertainty estimates for interpolation or spatial prediction (also kno