I am playing with MOE package (yelp.github.io/MOE) - I try to optimize some function of one variable, adding one point for sample at a time. Here is the intermediate chart I got:

Blue line is the actual function I optimize, red line - posterior mean from Gaussian Process. Green lines - standard deviations.

In my opinion, there is a huge problem here, because according to Bayesian optimization approach (as I understand it), samples are supposed to be taken from GP with prior constant mean (near zero).

And here I see posterior mean far from zero. It is very unlikely that this mean was taken from prior GP with near-zero constant mean. Changing covariance parameters doesn't help, I didn't see any effect at all. Noise is zero.

Any ideas on this (strange) behavior?