Gaussian processes - Stanford University
as Gaussian process regression. The material covered in these notes draws heavily on many different topics that we discussed previously in class (namely, the probabilistic interpretation oflinear regression1, Bayesian methods2, kernels3, andproperties ofmultivariate Gaussians4). The organization of these notes is as follows.
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