Create New Lower Dimension Gaussian Process

[SamChill]

I have fit a Gaussian Process to noisy experimental data y = f(x,y,z). I used SingleTaskGP and it does a great job modeling the experimental results. Then want to perform a Bayesian optimization where I find the candidates that maximize g(x,y) = f(x,y,10) - f(x,y,0). I believe that this new g(x,y) should be able to be analytically determined from the original gaussian process and represented as a custom Model that implements a posterior method that returns a new MultivariateNormal.

Can any one suggest how to make a custom model that computes posterior for the difference between two points on an already fit Gaussian Process? Or is there a better way to achieve my goal? I have been able to do this, at least for the means and I can make a contour plot that looks good, but I am getting confused about how to implement this in a way that optimize_acqf likes as it passes multiple batches at once.