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Prediction with Gaussian processes: From linear regression to linear prediction and beyond. (English) Zbl 0921.62121
Jordan, Michael I. (ed.), Learning in graphical models. Proceedings of the NATO ASI, Ettore Maiorana Centre, Erice, Italy, September 27 - October 7, 1996. Dordrecht: Kluwer Academic Publishers. NATO ASI Series. Series D. Behavioural and Social Sciences. 89, 599-621 (1998).
Summary: The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems.
For the entire collection see [Zbl 0889.00024].

62M20 Inference from stochastic processes and prediction
62F15 Bayesian inference
62J05 Linear regression; mixed models