Gaussian Processes for Machine Learning (2006) [pdf]

abhgh

This is the definitive reference on the topic! I have some notes on the topic as well, if you want something concise, but that doesn't ignore the math [1].

[1] https://blog.quipu-strands.com/bayesopt_1_key_ideas_GPs#gaus...

C-x_C-f

These are very cool, thanks. Do you know what kind of jobs are more likely to require Gaussian process expertise? I have experience in using GP for surrogate modeling and will be on the job market soon.

Also a resource I enjoyed is the book by Bobby Gramacy [0] which, among other things, spends a good bit on local GP approximation [1] (and has fun exercises).

[0] https://bobby.gramacy.com/surrogates/surrogates.pdf

[1] https://arxiv.org/abs/1303.0383

abhgh

Aside from secondmind [1] I don't know of any companies (only because I haven't looked)... But if I had to look for places with strong research culture on GPs (I don't know if you're) I would find relevant papers on arxiv and Google scholar, and see if any of them come from industry labs. If I had to take a guess on Bayesian tools at work, maybe the industries to look at would be advertising and healthcare.I would also look out for places that hire econometricists.

Also thank you for the book recommendation!

[1] https://www.secondmind.ai/

memming

Stationary GPs are just stochastic linear dynamical systems. (Not just the Matern covariance kernel)

FL33TW00D

For the visually inclined: https://distill.pub/2019/visual-exploration-gaussian-process...

tomhow

On the HN front page for 16 hours (though with strangely little discussion) just two days ago:

A Visual Exploration of Gaussian Processes (2019) - https://news.ycombinator.com/item?id=44919831 - Aug 2025 (1 comment)