Statistical inference for random T-tessellations models: application to agricultural landscape modeling.
Résumé
The Gibbsian T-tessellation models allow the representation of a wide range of spatial patterns. In this talk we present statistical tools for these models and illustrate their application to the comparison of three agricultural landscapes in France. Model parameters are estimated via Monte Carlo Maximum Likelihood based on an adapted Metropolis-Hastings-Green dynamics. In order to reduce the computational costs, a pseudolikelihood estimate is used for the initialization of the likelihood optimization. Model assessment is based on global envelope tests applied to the set of functional statistics of tessellation.
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