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Bayesian Reasoning and Machine Learning by David Barber

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19

Gaussian processes

In Bayesian linear parameter models, we saw that the only relevant quantities are related to the scalar product of data vectors. In Gaussian processes we use this to motivate a prediction method that does not necessarily correspond to any ‘parametric’ model of the data. Such models are flexible Bayesian predictors.

19.1   Non-parametric prediction

Gaussian Processes (GPs) are flexible Bayesian models that fit well within the probabilistic modelling framework. In developing GPs it is useful to first step back and see what information we need to form a predictor. Given a set of training data

image

(19.1.1)

where xn is the ...

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