Calculation of confidence bounds for Gaussian process models seems incorrect

[gkronber]

The confidence bounds shown in the line chart do not correspond with sigma noise in the model (see attachment).

[gkronber]

constant model (noise sigma = 0.65)

[gkronber]

r13121: calculate the variance for the noisy test data instead V(y*) instead of the variance for the posterior GP function V(f*)

[mkommend]

Reviewed r13121. OK.

The changes in the GP solution line chart are pretty clear. One thing that came to my mind while reviewing is that it should be possible to specify the confidence interval (default is set to 0.95).

The change in the GP-Model class is hard to judge without actually reading the paper (which I don't have at hand). I'll trust you that the noise variance has to be added to calculate the variance of the estimated values.

[gkronber]

Rasmussen et al. "Gaussian Processes for Machine Learning" chapter 2, page 19: "The algorithm returns the predictive mean and variance for noise free test data - to compute the predictive distribution for noisy test data y∗, simply add the noise variance σ²n to the predictive variance of f∗." (​http://www.gaussianprocess.org/gpml/chapters/RW2.pdf)

[gkronber]

r13145: merged r13121 from trunk to stable