This paper derives a criterion for deciding conditional independence that is consistent with small-sample corrections of Akaike's information criterion but is easier to apply to such problems as selecting variables in canonical correlation analysis and selecting graphical models. The criterion reduces to mutual information when the assumed distribution equals the true distribution; hence, it is called mutual information criterion (MIC). Although small-sample Kullback–Leibler criteria for these selection problems have been proposed previously, some of which are not widely known, MIC is strikingly more direct to derive and apply.

中文翻译：

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应用于典型相关分析和图形模型的互信息准则


本文推导了一个判断条件独立性的准则，该准则与赤池信息准则的小样本修正一致，但更容易应用于典型相关分析中变量选择和图模型选择等问题。当假设分布等于真实分布时，该标准简化为互信息；因此，它被称为互信息准则（MIC）。尽管之前已经提出了针对这些选择问题的小样本 Kullback-Leibler 标准，其中一些标准并未广为人知，但 MIC 的推导和应用明显更加直接。