论文标题
分级功能的最大相对差异原理
Maximum Relative Divergence Principle for Grading Functions on Power Sets
论文作者
论文摘要
一个分级功能与另一个分级功能相对差异的概念从完全有序的链扩展到有限事件空间的功率集。香农熵概念扩展到此类功率集上的归一化分级功能。将最大相对差异原理作为最大熵原理的概括作为确定“最合理的”分级函数的工具，并在某些操作研究应用中使用，在某些操作研究应用中，该函数被认为是在应用特定线性约束下该功能的“元素 - addive”或“依赖”。
The concept of Relative Divergence of one Grading Function from another is extended from totally ordered chains to power sets of finite event spaces. Shannon Entropy concept is extended to normalized grading functions on such power sets. Maximum Relative Divergence Principle is introduced as a generalization of the Maximum Entropy Principle as a tool for determining the "most reasonable" grading function and used in some Operations Research applications where that function is supposed to be "element-additive" or "cardinality-dependent" under application-specific linear constraints.
