Abstract
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 in 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.
Original language | English |
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Title of host publication | Learning in graphical models |
Editors | Michael Irwin Jordan |
Publisher | MIT |
Pages | 599-621 |
Number of pages | 23 |
ISBN (Print) | 0262600323 |
Publication status | Published - 1999 |
Keywords
- Gaussian processes
- Bayesian linear regression
- hierarchical modelling
- covariance functions
- neural network