EXACT SIMULATION OF A BOOLEAN MODEL
DOI:
https://doi.org/10.5566/ias.v32.p101-105Keywords:
Boolean model, importance sampling, Minkowsky functionals, Steiner formulaAbstract
A Boolean model is a union of independent objects (compact random subsets) located at Poisson points. Two algorithms are proposed for simulating a Boolean model in a bounded domain. The first one applies only to stationary models. It generates the objects prior to their Poisson locations. Two examples illustrate its applicability. The second algorithm applies to stationary and non-stationary models. It generates the Poisson points prior to the objects. Its practical difficulties of implementation are discussed. Both algorithms are based on importance sampling techniques, and the generated objects are weighted.References
Lantu´ejoul C (2002). Geostatistical simulation. Models
and algorithms. Berlin: Springer.
Matheron G (1975). Random sets and integral geometry.
New York:Wiley.
Miles R E (1969). Poisson flats in euclidean spaces. Adv.
Appl. Prob., Vol. 1, pp 211-237.
Miles R E (1974). A synopsis of Poisson flats in
euclidean spaces. In Stochastic geometry (Harding E F
and Kendall D G, eds.). New York:Wiley, pp. 202-227.
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Published
2013-06-27
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Original Research Paper
How to Cite
Lantuéjoul, C. (2013). EXACT SIMULATION OF A BOOLEAN MODEL. Image Analysis and Stereology, 32(2), 101-105. https://doi.org/10.5566/ias.v32.p101-105
