Conditional sampling for max-stable random fields
主 题: Conditional sampling for max-stable random fields
报告人: 王一早 （Univ. of Michigan Ann Arbor）
时 间: 2011-12-26 15:00-16:00
地 点: 理科一号楼 1418
Max-stable random fields play a central role in modeling extreme value phenomena. We obtain an explicit formula for the conditional probability of general max-linear models, which include a large class of max-stable random fields. Our formula yields a conditional sampling algorithm, which is seemingly connected to the NP-hard set cover problem from computer science. However, we reveal that the probabilistic structure of the max-linear models can lead to significant combinatorial simplification, and consequently an efficient algorithm for exact sampling from the conditional distributions. Our method provides a computational solution to the prediction problem for spectrally discrete max-stable random fields. This work offers new tools and a new perspective to many statistical inference problems for spatial extremes, arising for example in meteorology, geology, and environmental applications. This is a joint work with Stilian Stoev.