TITLE: Gradients of marginal likelihood of Gaussian Process with squared exponential covariance, for learning hyper-parameters
QUESTION [15 upvotes]: The derivation of gradient of the marginal likelihood is given in http://www.gaussianprocess.org/gpml/chapters/RW5.pdf
But the gradient for the most commonly used covariance function, squared exponential covariance, is not explicitly given. 
I am implementing the Rprop algorithm in http://ml.informatik.uni-freiburg.de/_media/publications/blumesann2013.pdf for learning hyper-parameters sigma (signal variance) and h (length). Alas, my implementation is not working well. I have derived the gradients but I am not sure if they are correct.
Can someone point me to a good tutorial / article that explicitly give the expressions for the hyper parameter gradients?

REPLY [2 votes]: Maybe there is also an error in the gradient formulation, because in Rasmussen&Williams - Gaussian Process for Machine Learning, p.114, eq 5.9, it is expressed as:
Partial deivatives log marginal likelihood w.r.t. hyperparameters  
where the 2 terms have different signs and the y targets vector is transposed just the first time.