SparseGPRegression, is it possible to include observation weights?
Basically, I have data that age over time, the most resent measures are the ones closest to the current state.
I was thinking that it might be possible to change
X from being a list of floats to a list of tuples.
x = [0.1, 0.2, 0.3] weights = [1.0, 0.9, 0.8] x_weighted = list(zip(x, weights))
But I’m sceptic that this would actually do what I want it to. I want the functions to put more focus on being accurate with the observations that has a high weight, and vice versa.