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Conditional simulation of a positive random vector subject to max-linear constraints. A geometric perspective

Abstract : Predicting natural phenomena modeled by max-stable random fields with Fréchet margins is not simple because these models do not possess finite first and second order moments. In such situations, a Monte Carlo approach based on conditional simulations can be considered. In this paper we examine a recent algorithm set up by Wang and Stoev to conditionally simulate a max-stable random field with discrete spectrum. Besides presenting this algorithm, we provide it with a geometric interpretation and put emphasis on several implementation details to obviate its combinatorial complexity. Along the way, a number of other critical issues are mentioned that are not often addressed in the current practice of conditional simulations. An illustrative example is given.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00766156
Contributor : Christian Lantuéjoul <>
Submitted on : Monday, December 17, 2012 - 5:02:25 PM
Last modification on : Wednesday, June 16, 2021 - 3:48:20 AM

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  • HAL Id : hal-00766156, version 1

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Christian Lantuéjoul, Francis Maisonneuve, Jean-Noël Bacro, Liliane Bel. Conditional simulation of a positive random vector subject to max-linear constraints. A geometric perspective. Ninth International Geostatistics Congress, Jun 2012, Oslo, Norway. 6 p. ⟨hal-00766156⟩

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