Mutual information is a widely-used information theoretic measure to quantify the amount of association between variables. It is used extensively in many applications such as image registration, diagnosis of failures in electrical machines, pattern recognition, data mining and tests of independence. The main goal of this paper is to provide an efficient estimator of the mutual information based on the approach of Al Labadi et. al. (2021). The estimator is explored through various examples and is compared to its frequentist counterpart due to Berrett et al. (2019). The results show the good performance of the procedure by having a smaller mean squared error.
翻译:信息是用来量化变量之间关联程度的一种广泛使用的信息理论性措施,在图像登记、电机故障诊断、模式识别、数据挖掘和独立测试等许多应用中广泛使用,本文件的主要目的是根据Al Labadi等人(2021年)的做法,为相互信息提供高效的估算数据,通过各种实例探讨估算数据,并与Berrett等人(2019年)的常客对应数据进行比较,结果显示,由于中度差错较小,程序表现良好。