文章基本信息

标题：How consistent is my model with the data? Information-Theoretic Model Check ⁎
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作者：Andreas Svensson ; Dave Zachariah ; Thomas B. Schön 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：407-412
DOI：10.1016/j.ifacol.2018.09.179
语种：English
出版社：Elsevier
摘要：AbstractThe choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-box. We develop a method to evaluate the specified model class by assessing its capability of reproducing data that is similar to the observed data record. This model check is based on the information-theoretic properties of models viewed as data generators and is applicable to e.g. sequential data and nonlinear dynamical models. The method can be understood as a specific two-sided posterior predictive test. We apply the information-theoretic model check to both synthetic and real data and compare it with a classical whiteness test.
关键词：KeywordsDynamic modelsInformation theoryModel testsNonlinear modelsPortmanteau testModel criticism