In Chapter 20, we considered basis function methods such as splines and kernel regressions, which typically require choice of a somewhat arbitrary set of knots. One can prespecify a grid of many knots and then use variable selection and shrinkage to effectively discard the knots that are not needed, but there may nonetheless be some sensitivity to the initial grid. A high-dimensional grid leads to a heavy computational burden, while a low-dimensional grid may not be sufficiently flexible. Another possibility, which has some distinct computational and theoretical advantages, is to set up a prior distribution for the regression function using a Gaussian process, a flexible class of models for which any finite-dimensional marginal distribution is Gaussian, and which can be viewed as a potentially infinite-dimensional generalization of Gaussian distribution.