[SciPy-User] kriging module

Gael Varoquaux gael.varoquaux@normalesup....
Sat Nov 20 17:08:56 CST 2010

On Sat, Nov 20, 2010 at 04:59:41PM -0600, Robert Kern wrote:
> > Sorry, I should have said 'Gaussian process regression', which is the
> > full name, and is an equivalent to Kriging. Gaussian processes in
> > themself are a very large class of probabilistic models.
> > AFAICT, PyMC does not have any Gaussian process regression, and it does
> > seem a bit outside its scope.

> I'm pretty sure it does. See section 1.4 "Nonparametric regression"
> and 2.4 "Geostatistical example" in the GP User's Guide:

>   http://pymc.googlecode.com/files/GPUserGuide.pdf

Yes, you are right. My bad. The good news is that it means that the name
is not too badly overloaded.

I see that they do the estimation by sampling the posterior, whereas the
proposed contribution in the scikit simply does a point estimate using
the scipy's optimizers. I guess that PyMC's approach gives a full
posterior estimate, and is thus richer than the point estimate, but I
would except it to be slower. I wonder if they are any other fundemental
differences (I don't know Gaussian processes terribly well).


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