Hi everyone! I am kind of new to pymc and tech in general so sorry if this question is not very good.
I’ve created a Bayesian Model with a Gaussian Process kernel. I also split my data into a training and a test set. Here is the code where I put the data into the model (under “with pm.model as model:”)
# Link the latent function to the observed data
y_ = gp.prior(“y”, X=X_train, shape=(n,))
# Add the likelihood
pm.Normal(“obs”, mu=y_, sigma=sigma, observed=y_train)
Afterwards, I want to use the bayesian model to make predictions on my test data, but I cannot figure out how to do so with sample_posterior_predictive, or do so such that I can make predictions while having a quantification of uncertainty, with the Bayesian model. Can someone explain how to do this? I tried to use pymc.set_data, but either it is no longer working or I am not implementing it correctly. Thanks in advance!