A small question
In the book of Rasmussen Page 115 last paragraph. When we have multiple databases you setup a gaussian for each database and the optimisation is said can be done by adding the likelihoods as a single one.
What about the gradients? do I add the gradients?
If I have a simple gaussian with three hyperparameters and I using minimize I will have a function to optimise with 4 hyperparameters, the function and three gradients. is suppose that I just add the gradients? am i correct?
EDIT: Why I am asking this: I have a robot with 6 DoF highly couple. a dataset is a movement in x,y plane, second dataset is a movement in other plane. I am using multioutput gaussians processes.