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I am using the MATLAB code for Rasmussen & Williams' book Gaussian Processes for Machine Learning.

How can one incorporate prior knowledge in Gaussian process regression? Say, that the variance in one dimension of a two dimensional vector is greater. Is it only by considering the parameters of a normal distribution, or can it be more detailed?

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  • $\begingroup$ The prior is Gaussian. Do you have the actual book? If not, you can read the co-author's summary. Hanna Wallach also has an introductory presentation $\endgroup$
    – Emre
    Commented May 22, 2012 at 3:53
  • $\begingroup$ Then it is not really a prior knowledge ! If I already know that the influence of variable A is more than variable B, then how should I consider it ?! $\endgroup$
    – Areza
    Commented May 23, 2012 at 22:47
  • $\begingroup$ so basically it is not possible ?! $\endgroup$
    – Areza
    Commented Jun 3, 2012 at 12:59

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Have a look at Gelman et.al. Bayesian Data Analysis (Third Edition), chapter 21 which has a Bayesian treatment of Gaussian processes. It also has good applied examples.

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