The multiscale Fisher's independence test (MULTIFIT hereafter) proposed by Gorsky & Ma (2022) is a novel method to test independence between two random vectors. By its design, this test is particularly useful in detecting local dependence. Moreover, by adopting a resampling-free approach, it can easily accommodate massive sample sizes. Another benefit of the proposed method is its ability to interpret the nature of dependency. We congratulate the authors, Shai Gorksy and Li Ma, for their very interesting and elegant work. In this comment, we would like to discuss a general framework unifying the MULTIFIT and other tests and compare it with the binary expansion randomized ensemble test (BERET hereafter) proposed by Lee et al. (In press). We also would like to contribute our thoughts on potential extensions of the method.

翻译：Gorsky & Ma (2022年) 提议的多尺度渔业独立测试(MultiFIT)(以下称MLUTFIT)(2022年)是测试两种随机矢量之间独立性的一种新颖方法。根据它的设计,这一测试对于检测当地依赖性特别有用。此外,通过采用无抽样方法,它很容易容纳大量的样本大小。拟议方法的另一个好处是能够解释依赖性的性质。我们祝贺作者Shai Gorksy和Li Ma, 因为他们的工作非常有趣和优雅。我们想在此评论中讨论一个将MUTIFIT和其他测试统一起来的总框架,并将它与Lee等人(新闻界)提出的二进制随机混合测试(BERET)进行比较。我们还想就该方法的潜在扩展提出我们的想法。