Abstract
The sequential generation of random functions (R.F.) is of interest for simulating the change of spatial structures with time, and for finding satisfactory models for spatial data. We introduce some rules for constructing sequential R.F., and present statistical properties of markovian jumps sequential random functions, and digital dead leaves models. The R.F. simulate imbricated structures, such as encountered in perspective views, where there is a partial coverage of individual features. They are easy to simulate, and they also provide isofactorial models. The same construction as for the digital dead leaves model, but with different elementary rules of combination, provides other well-known models : the boolean R.F. and the dilution R.F.
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© 1989 Springer Science+Business Media Dordrecht
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Jeulin, D. (1989). Sequential Random Functions Models. In: Armstrong, M. (eds) Geostatistics. Quantitative Geology and Geostatistics, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-6844-9_13
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DOI: https://doi.org/10.1007/978-94-015-6844-9_13
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