James Lee has a new post on his tcsmath blog about Gaussian Processes, a topic I’ve been enamored with for the last while. I love the graphics he includes in his posts… they look like they take a lot of work to produce.
James (and Talagrand) are interested in finding the supremum of a GP, which I can imagine being a very useful tool for studying random graphs and average-case analysis of algorithms. I’m interested in finding rapidly mixing Markov chains over GPs, which seems to be useful for disease modeling. Seemingly very different directions of research, but I’ll be watching tcsmath for the next installment of majorizing measures.
One response to “Gaussian Processes in Theory”
This might be related: http://arxiv.org/abs/1005.0812