Measures of Dependence in Normal Models and Exponential Models by Information Gain
研究了基于信息增益原理的依赖度量在正态模型和几种二元指数模型中的表现，发现该原理在正态模型中通常给出良好度量，在部分指数模型中也产生有趣结果。
The principle of obtaining a measure of dependence based on the notion of information gain is examined for normal models and for several bivariate exponential models. It is shown that, under normal models, the principle usually gives good measures. It is also shown that it gives interesting measures for some exponential models.