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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