Hi list, Did anyone ever considered using the Cramer-Rao lower bound estimate for the variance-covariance matrix of the GaussianProcess hyperparameters estimate?
I have seen the gradient of the marginal log likelihood is already available. How about the hessian matrix? Looking at the theta values wrt to the one in the fitted kernel themselves, it looks like some normalization occurs, which is fine, though how do I get the true gradient back? Actually, I am more interested in infering the parameters rather than predicting. I have considered using pymc3 but MCMC is quite time expensive and I would like to be able to speed this with a reasonable approximation. George is also an alternative but out of the question since I am running Windows. Thank you, Vincent
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