[pymvpa] Optimising hyper parameters for Gaussian processes

[Martin Fergie]

Hi,

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
parameters.

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,
Martin
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