翻訳と辞書 Words near each other ・ Convergence of measures ・ Convergence of random variables ・ Convergence Party ・ Convergence problem ・ Convergence Review ・ Convergence Technologies Professional ・ Convergence tests ・ Convergence trade ・ Convergence zone ・ Convergence-divergence zone ・ Convergences 2007 ・ Convergencia Sindical ・ Convergent ・ Convergent boundary ・ Convergent charging ・ Convergent cross mapping ・ Convergent encryption ・ Convergent evolution ・ Convergent extension ・ Convergent Functional Genomics ・ Convergent Linux Platform ・ Convergent matrix ・ Convergent series ・ Convergent Series (short story collection) ・ Convergent synthesis ・ Convergent Technologies ・ Convergent Technologies Operating System ・ Convergent thinking ・ Convergent validity ・ Converging Conspiracies Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Convergent cross mapping ： ウィキペディア英語版 Convergent cross mapping Convergent cross mapping (CCM) is a statistical test for a cause-and-effect relationship between two time series variables that, like the Granger causality test, seeks to resolve the problem that correlation does not imply causation. While Granger causality is best suited for purely stochastic systems where the influences of the causal variables are separable (independent of each other), CCM is based on the theory of dynamical systems and can be applied to systems where causal variables have synergistic effects. The test was developed in 2012 by the lab of George Sugihara of the Scripps Institution of Oceanography, La Jolla, California, USA.〔(Michael Marshall in New Scientist magazine 2884: Causality test could help preserve the natural world, 28 September 2012 )〕 ==Theory== Convergent cross mapping is based on Takens' embedding theorem, which states that generically the attractor manifold of a dynamical system can be reconstructed from a single observation variable of the system, $X$. This reconstructed or shadow attractor $M\_X$ is diffeomorphic (has a one-to-one mapping) to the true manifold, $M$. Consequently, if two variables X and Y belong to the same dynamics system, the shadow manifolds $M\_X$ and $M\_Y$ will also be diffeomorphic (have a one-to-one mapping). Time points that are nearby on the manifold $M\_X$ will also be nearby on $M\_Y$. Therefore, the current state of variable $Y$ can be predicted based on $M\_X$. Cross mapping need not be symmetric. If $X$ forces $Y$ unidirectionally, variable $Y$ will contain information about $X$, but not vice versa. Consequently, the state of $X$ can be predicted from $M\_Y$, but $Y$ will not be predictable from $M\_X$. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Convergent cross mapping」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.