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RE: [ai-geostats] natural neighbor applied to indicator transforms

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  • Gregoire Dubois
    Ciao Sebastiano, I realized nobody replied to your question (sorry for have added confusion here). I don t see any objection in applying any interpolator to
    Message 1 of 7 , Sep 5, 2005
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      Ciao Sebastiano,
       
      I realized nobody replied to your question (sorry for have added confusion here).
       
      I don't see any objection in applying any interpolator to probability values.
      However, you should better use exact interpolators to avoid getting probabilities of occurences > 1 (or smaller than 0)
       
      Cheers
       
      Gregoire
       
       
      -----Original Message-----
      From: seba [mailto:sebastiano.trevisani@...]
      Sent: 02 September 2005 10:07
      To: ai-geostats@...
      Cc: ai-geostats@...; 'Nicolas Gilardi'
      Subject: RE: [ai-geostats] natural neighbor applied to indicator transforms


      I try to reformulate my question.....
      When performing direct (i.e. without crossvariogram) indicator kriging, practically we interpolate probability values by means of ordinary kriging. These probability values could represent the probability of occurrence of some category or the probability to overcome some threshold.
      My question is: is there anything wrong to interpolate these probability values with other interpolating algorithm like, for example natural neighbor (or triangulation)?
      In my opinion is all ok ..... considering also that we have no problem of order relation violations.
      Again, this technique is applied only for a preliminary data analysis

      Then a short consideration directed about the importance of boundaries:
      Quoting Nicolas Gilardi
      "My personnal feeling about the distinction between using a classification algorithm or a regression one is the importance you put on the boundaries.If you look for smooth boundaries, with uncertainty estimations, etc., then a regression algorithm (like indicator kriging) is certainly a good approach."

      Well, if you use fuzzy classification the boundaries become continuos...fuzzy.

      Bye

      S. Trevisani
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