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信息可区分性及其在失效数据分析中的应用AI 解读

In maximum entropy (ME) modeling, the information discrepancy between two distributions is measured in terms of their entropy difference. In discrimination information statistics the information discrepancy between two distributions is measured in terms of the Kullback-Leibler function (i.e., relative entropy or cross-entropy). This article presents an equivalence between Kullback–Leibler functions and entropy differences involving an ME distribution. Based on this…

信息可区分性及其在失效数据分析中的应用AI 解读

In maximum entropy (ME) modeling, the information discrepancy between two distributions is measured in terms of their entropy difference. In discrimination information statistics the information discrepancy between two distributions is measured in terms of the Kullback-Leibler function (i.e., relative entropy or cross-entropy). This article presents an equivalence between Kullback–Leibler functions and entropy differences involving an ME distribution. Based on this…

指定年龄下生存分布的“新优于旧”性质检验AI 解读

The cumulative distribution function F, of a nonnegative random variable, is said to be new better than used of specified age t0 if F¯(x+t0)≤F¯(x)F¯(t0) for all x≥0⁠, where F¯=1-FLet We propose a class of test statistics Tk, indexed by a positive integer k, for testing the null hypothesis that F belongs to A against the alternative hypothesis that F is new better than used of specified age t0 but not in A. We compare the performance of our proposed test statistics with the…