Hello, I am trying to perform inference on the following model to get posterior distributions on some parameters of a black-box function and the length-scale of a gaussian process. The function eta(theta,x) can be any arbitrary black box function, where theta are the parameters to be inferred and x…

Sorry I’m late, yup, totally agree with @DanhPhan ’s suggestions. You can definitely use the GP covariance functions without the any of the other stuff in the GP library, like this: cov = pm.gp.cov.ExpQuad(1, ls=lengthscale) Sigma = cov(X) likelihood = pm.MvNormal('likelihood', mu=mu, cov=Sigma, o…

Adding Black box Model to GP

Questions v3

Rahul_Rameshbabu May 8, 2022, 7:36pm 2

@bwengals , after some reading on this forum, I can see you are extremely knowledgeable about Gaussian Processes, so I would be super grateful for your input

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Adding Black box Model to GP

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