Use saved gaussian model from sci-kit in pymc3?

Questions
4

You will have to make the GP process again in pymc3, but you can set the parameters using the fitted parameters from scikit-learn (or build the prior accordingly using these fitted parameters). That’s what I meant

with model: # the model you define above with the Q1, Q2, Q3
    cov = Q1**2 * pm.gp.cov.Matern52(1, Q2)
    gp = pm.gp.Marginal(cov_func=cov)
    y_ = gp.marginal_likelihood("y", X=X, y=y, noise=Q3)

Q1, Q2, Q3 are the parameters from the pretrained scikit-learn GP.