• 前言
• 第一章 引言
• 第二章 解释性
• 第三章 数据集
• 第四章 解释模型
• 第五章 模型不可知论方法
• 第六章 基于实例的解释
• 第七章 神经网络解释
• 第八章 水晶球
• 第九章 贡献
• 第十章 引用本书

### 最新论文

[ABRIDGED] The Cash statistic, also known as the C stat, is commonly used for the analysis of low-count Poisson data, including data with null counts for certain values of the independent variable. The use of this statistic is especially attractive for low-count data that cannot be combined, or re-binned, without loss of resolution. This paper presents a new maximum-likelihood solution for the best-fit parameters of a linear model using the Poisson-based Cash statistic. The solution presented in this paper provides a new and simple method to measure the best-fit parameters of a linear model for any Poisson-based data, including data with null counts. In particular, the method enforces the requirement that the best-fit linear model be non-negative throughout the support of the independent variable. The method is summarized in a simple algorithm to fit Poisson counting data of any size and counting rate with a linear model, by-passing entirely the use of the traditional $\chi^2$ statistic.

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