[SciPy-User] scipy.interpolate.rbf sensitive to input noise ?

[josef.pktd at gmail.com]

On Tue, Feb 23, 2010 at 12:57 PM,  <josef.pktd at gmail.com> wrote:
> On Tue, Feb 23, 2010 at 12:46 PM, Robert Kern <robert.kern at gmail.com> wrote:
>> On Tue, Feb 23, 2010 at 11:43, denis <denis-bz-gg at t-online.de> wrote:
>>> Robert, Josef,
>>>  thanks much for taking the time to look at RBF some more.
>>> Summary, correct me:
>>>    A - smooth*I in rbf.py is a sign error (ticket ?)
>>
>> Not necessarily. It seems to work well in at least some cases. Find a
>> reference that says otherwise if you want it changed.
>
> chapter 2 page 16, for gaussian process. As I said I don't know about
> the other methods
>

http://docs.google.com/viewer?a=v&q=cache:qs8AaAxO6nkJ:www.gaussianprocess.org/gpml/chapters/RW2.pdf+gaussian+process+noise+Ridge&hl=en&gl=ca&pid=bl&srcid=ADGEESj4j8osT6cOIc65r3OaeAtQO_dzgZD4YxSAEkFTeRZajBcROJpJJ9zTlMSrD2OaK1iOJYgy8QqH_Nr0rNxf41faNihCdIzWyVOYxtCFIR7H8mdQZAKFoeaRkFamQlCKhp_s1FOI&sig=AHIEtbQK35MLfnZAySw3lF-dR_mNcSaP3w

google links are very short, missed a part

>
> www.gaussianprocess.org/gpml/chapters/RW2.pdf
>
> Josef
>>
>>>    for gauss, start with A + 1e-6*I  to move eigenvalues away from 0
>>>    others have pos/neg eigenvalues, don't need smooth.
>>
>> No. If you need a smoothed approximation to noisy data rather than
>> exact interpolation than you use the smooth keyword. Otherwise, you
>> don't.
>>
>> --
>> 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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>