How to make GP with non-Gaussian likelihood more efficient?

Modeling
22

  1. How to select the number of basis function? I selected it by looking at Figure 6 (https://github.com/gabriuma/basis_functions_approach_to_GP/blob/master/Paper/manuscript/manuscript_06.pdf): I only knew the normalised length scale is greater than 0.05 and took c=1.5, so I used 40 basis functions, but I also tried 30 basis functions, and there are not much differences in terms of performance.

  2. I adapted the code to be ‘optimal’ and it do run fast than my previous code. Thanks! However, when I change poisson_log to poisson_log_glm, it becomes slower. Is it due to my additional term in my design matrix which changes over iterations? gp1.stan (4.1 KB)

  3. I found spline method s( , bs=‘gp’) is faster, but if I understand correctly, this does not estimate hyperparameters of the kernel and I need to fix them as input, right? Is it possible to use this method while estimating the hyper parameters?

  4. You mentioned New adaptive warmup proposal (looking for feedback)! However, I think it only helps with the warm-up period, isn’t it?

Sorry for my late reply as I just tried these options and thank you very much for your potential help!

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