String Gaussian Process Kernels

Kernels are often used as a flexible way of departing from linear hypotheses in learning machines, thereby allowing for more complex nonlinear patterns [1, 2]. They have indeed been successfully applied to problems of classification, clustering, density estimation and regression. The duality between kernels and covariance functions has made kernels a critical tool for both frequentist and Bayesian statisticians. In the Bayesian nonparametrics community, kernels are often used as a covariance function of a Gaussian process (GP), introduced as a prior over a latent function. The family of covariance functions postulated for the GP is typically chosen so as to express prior domain knowledge about the underlying function, such as periodicity, regularity and range.