[pymvpa] Optimising hyper parameters for Gaussian processes
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,
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