Statistical inference for random T-tessellations models. Application to agricultural landscape modeling

Author:

Adamczyk-Chauvat Katarzyna,Kassa Mouna,Papaïx Julien,Kiêu Kiên,Stoica Radu S.

Publisher

Springer Science and Business Media LLC

Reference33 articles.

1. Adamczyk-Chauvat, K., Kiêu, K. (2015). LiTe. http://kien-kieu.github.io/lite

2. Arak, T., Clifford, P., Surgailis, D. (1993). Point-based polygonal models for random graphs. Advances in Applied Probability, 25, 348–372.

3. Baddeley, A., Rubak, E., Turner, R. (2016). Spatial Point Patterns: Methodology and Applications with R. Boca Raton: Chapman and Hall/CRC Press.

4. Dereudre, D. (2019). Introduction to the theory of Gibbs point processes. In D. Coupier (Ed.), Stochastic Geometry. Cham: Springer International Publishing.

5. Dereudre, D., Lavancier, F. (2011). Practical simulation and estimation for Gibbs Delaunay-Voronoi tessellations with geometric hardcore interaction. Computational Statistics and Data Analysis, 55, 498–519.

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