Ryan May wrote:
> On Sun, Oct 4, 2009 at 4:21 PM, Robert Kern <robert.k...@gmail.com> wrote:
>> On 2009-10-04 15:27 PM, Christopher Barker wrote:
>>> Václav Šmilauer wrote:
>>>
>>>> about a year ago I developed for my own purposes a routine for averaging
>>>> irregularly-sampled data using gaussian average.
>>> is this similar to Kernel Density estimation?
>>>
>>> http://www.scipy.org/doc/api_docs/SciPy.stats.kde.gaussian_kde.html
>> No. It is probably closer to radial basis function interpolation (in fact, it
>> almost certainly is a form of RBFs):
>>
>> http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#id1
> 
> Except in radial basis function interpolation, you solve for the
> weights that give the original values at the original data points.
> Here, it's just a inverse-distance weighted average, where the weights
> are chosen using an exp(-x^2/A) relation.  There's a huge difference
> between the two when you're dealing with data with noise.

Fair point.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco


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