Tag Archives: gpr

Non-parametric regression in Python: Gaussian Processes in sklearn (with a little PyMC)

I’ve got a fun class going this quarter, on “artificial intelligence for health metricians”, and the course content mixed with some of the student interest has got me looking at the options for doing Gaussian process regression in Python. `PyMC2` has some nice stuff, but the `sklearn` version fits with the rest of my course examples more naturally, so I’m using that instead.

But `sklearn` doesn’t have the fanciest of fancy covariance functions implemented, and at IHME we have been down the road of the Matern covariance function for over five years now. It’s in `PyMC`, so I took a crack at mash-up. (Took a mash at a mash-up?) There is some room for improvement, but it is a start. If you need to do non-parametric regression for something that is differentiable more than once, but less than infinity times, you could try starting here: http://nbviewer.ipython.org/gist/aflaxman/af7bdb56987c50f3812b

p.s. Chris Fonnesbeck has some great notes on doing stuff like this and much more here: http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section5_1-Gaussian-Processes.ipynb

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Filed under statistics

Big Week for Healthy Algorithms Posts

I’ve been in meetings literally all day, but I’ve got so much to say that I’m still here… this is a newsletter that IHME put out yesterday, and it’s got me in the front cover photo. How can I pass up announcing that? It’s mostly for my mom, but PyMC fans might also appreciate the shoutout I managed to Anand Patil, who authored the PyMC Gaussian Process package that I’ve been urging people to use lately.


Filed under general