Due to the slow gradient calculation of gaussian process hyperparameters using autograd, I want to look into implementing my own version of the gradients (for a very limited set of covariance functions).

To do this I looked around and think that `guide`

s are what I am looking for.

How would I now define a guide and pass it to the training e.g. using the standard GP regression example? (http://pyro.ai/examples/gp.html)

Just for the sake of the example, let’s say I would just multiply each hyperparameter by 1.1 (i.e. add 10%), how should the guide look like?

Any directions and tips appreciated!