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Spatial data modelling and maximum entropy theory

Author

Listed:
  • D. Klimešová

    (Czech University of Agriculture, Prague, Czech Republic & Institute of Information Theory and Automation, Czech Academy of Sciences, Prague)

  • E. Ocelíková

    (Czech University of Agriculture, Prague, Czech Republic & Institute of Information Theory and Automation, Czech Academy of Sciences, Prague)

Abstract

Spatial data modelling and consequential error estimation of the distribution function are key points of spatial analysis. For many practical problems, it is impossible to hypothesize distribution function firstly and some distribution models, such as Gaussian distribution, may not suit to complicated distribution in practice. The paper shows the possibility of the approach based on the maximum entropy theory that can optimally describe the spatial data distribution and gives the actual error estimation.

Suggested Citation

  • D. Klimešová & E. Ocelíková, 2005. "Spatial data modelling and maximum entropy theory," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 51(2), pages 80-83.
    • Handle: RePEc:caa:jnlage:v:51:y:2005:i:2:id:5080-agricecon
    DOI: 10.17221/5080-AGRICECON
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