[pymvpa] Optimising hyper parameters for Gaussian processes

Martin Fergie mfergie at cs.man.ac.uk
Tue May 31 11:00:43 UTC 2011


I've been looking into using PyMVPA recently for performing Gaussian process
regression. I can't seem to find a method from gpr.py or the examples of
minimizing the log marginal likelihood with respect to the kernel hyper

Is there a recommended way of doing this? or would I have to implement some
sort of wrapper to combine gpr.py with a gradient ascent routine?

I currently use the GPML matlab package, however I'd like to replace it with
a python solution so I can easily parallelize training multiple Gaussian
processes over multiple machines.

Thanks for your help,
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/attachments/20110531/013aee79/attachment.html>

More information about the Pkg-ExpPsy-PyMVPA mailing list