Learning from Various Plants and Scenarios: Statistical Modeling

  • W. zu CastellEmail author
  • R. Matyssek
  • A. Göttlein
  • F. Fleischmann
  • A. Staninska
Part of the Ecological Studies book series (ECOLSTUD, volume 220)


Experimental approaches studying complex phenomena in nature often show various answers to one question, depending on the experimental scale chosen, the experimental set-up and other types of restrictions chosen along the way. This difficulty does not only result from a lack of experimental technology, but also from our reductionist approach. While having been shown to be very powerful in experimental sciences, the approach also faces limitations dealing with complex systems. Being aware of such difficulties, statistical methodology has to provide answers for various levels of contingency. We discuss some of these questions and look at examples of statistical methods according to their power in addressing questions raised from complexity.


Support Vector Machine Experimental Variogram Empirical Risk Minimization Ozone Fumigation Unknown Probability Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • W. zu Castell
    • 1
    Email author
  • R. Matyssek
    • 2
  • A. Göttlein
    • 3
  • F. Fleischmann
    • 4
  • A. Staninska
    • 1
  1. 1.Scientific Computing Research UnitGerman Research Center for Environmental Health, Helmholtz Center MunichNeuherbergGermany
  2. 2.Chair of Ecophysiology of PlantsTechnische Universttät MünchenFreisingGermany
  3. 3.Forest Nutrition and Water ResourcesTechnische Universttät MünchenFreisingGermany
  4. 4.Pathology of Woody PlantsTechnische Universttät MünchenFreisingGermany

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