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Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework
Computational Statistics ( IF 1.3 ) Pub Date : 2019-11-19 , DOI: 10.1007/s00180-019-00936-5
Guillaume Perrin , Christian Soize

This work considers the challenging problem of identifying the statistical properties of random fields from indirect observations. To this end, a Bayesian approach is introduced, whose key step is the nonparametric approximation of the likelihood function from limited information. When the likelihood function is based on the evaluation of an expensive computer code, this work also proposes a method to select iteratively new design points to reduce the uncertainties on the results that are due to the approximation of the likelihood. Two applications are finally presented to illustrate the efficiency of the proposed procedure: a first one based on analytic data, and a second one dealing with the identification of the random elasticity field of an heterogeneous microstructure.

中文翻译:

贝叶斯框架中间接识别随机场统计特性的自适应方法

这项工作考虑了从间接观察中识别随机场的统计特性这一具有挑战性的问题。为此,引入了贝叶斯方法,其关键步骤是从有限信息中对似然函数进行非参数逼近。当似然函数是基于对昂贵的计算机代码的评估时,这项工作还提出了一种方法,用于迭代选择新的设计点,以减少由于似然性导致的结果不确定性。最后提出了两个应用程序来说明所提出程序的效率:第一个程序基于分析数据,第二个程序用于识别异质微结构的随机弹性场。
更新日期:2019-11-19
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