Hey all - More specifically, hey GP people -
Writing up some stuff I’ve done over the summer at Aalto (although a bit late…). Aiming for an applied, pedagogical intro to applied Gaussian Processes in Stan, using a variety of open source data sets.
This is Part 1: Intro to GP notation, motivation behind GPs, some properties of kernels, GP regression and ARD.
Some things I could use help with:
Hyperparameter priors: I could at least use a length scale that uses the scale of the data, as in Betancourt’s case study.
Out of sample: Using Loo package instead of a naive RMSE.
I was trying to show the smoothness changes in different cases of Matern kernel, but the sample size to high/vizualizations too small to show the “wiggliness” of the kernels.
Part 2 is coming. It has some different models including GP classificaiton, GP time series (modeling directly and using a GP prior in GARCH), and then a survival model. I split it up because the case study was becoming too long.
applied_gaussian_processes_in_stan.pdf (419.7 KB)