Hi,<br><br>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. <br>
<br>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?<br><br>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. <br>
<br>Thanks for your help,<br><font color="#888888"><font color="#888888">Martin</font>
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