Hyperparameters in Gaussian Process My academical background is in physics and analysis (PDE's), but now I'am reading about data science. 

I'm reading about Gaussian Process implementation in Sci-Kit Learn

I could not find a simple explanation about those hyper-parameters theta0, thetaL and thetaU. Can someone explain to me what those parameters are, and how to choose then?
As you probably know, the Gaussian distribution is fully specified by its mean and covariance. theta0, thetaL, and thetaU are, respectively, initial values, lower bounds, and upper bounds on the covariance. You can set them based on your knowledge of the particulars of the problem. To learn more about Gaussian processes, I suggest visiting this great Web site. It even offers a free ebook.