I use Pyro’s
GPRegression model to approximate a function. When I print
model.named_parameters(), there is a parameter called
noise, which I understand as the difference between the ground truth and the observation. The value of this parameter is initially 0 (if I don’t set a prior), but is updated to a real scalar value (pos or neg) after training the model. I thought epsilon was iid N(0, sigma).
My question is: How can I interpret the noise value? If it is negative, do I underestimate the ground truth at every single point? Or is it the expected value of epsilon?