发表机构
University of Salerno（萨勒诺大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文对Yager概率分布否定及其推广开展信息论分析，利用信息论与优化理论工具整合强化其性质，为该否定定义提供理论依据，确立其在信息论准则下的合理性。
AI 中文摘要
在开创性论文（Yager 2015）中，Yager定义了概率分布$\boldsymbol{p}=(p_1,\boldsymbol{p}_n)$的否定，即分布$\boldsymbol{\bar{p}} = (\bar{p}_1,\boldsymbol{\bar{p}}_n)$，其中对$i=1,\boldsymbol{n}$，$\bar{p}_i = ({1-p_i})/({n-1})$。本文对Yager的否定及其推广进行了全面的信息论分析，利用信息论和优化理论工具，在统一框架下整合、扩展并强化了Yager否定的多项已知性质。总体而言，研究结果为Yager的否定提供了有力的理论依据，使其成为各类信息论准则下最自然、最具原则性的概率分布否定定义。
英文摘要
In the seminal paper (Yager 2015), Yager defined the negation of a probability distribution $\mathbf{p}=(p_1,\dots,p_n)$, as the distribution $\overline{\mathbf{p}} = (\overline{p}_1,\dots,\overline{p}_n)$, where $\overline{p}_i = ({1-p_i})/({n-1}),$ for $ i=1, \ldots , n.$ In this paper, we present a comprehensive information-theoretic analysis of Yager's negation and its generalizations. Using tools from information theory and majorization theory, we unify, extend, and strengthen a number of previously known properties of Yager's negation within a common framework. Overall, our results offer strong theoretical justification for Yager's negation as the most natural and principled definition of probability distribution negation under various information theoretic criteria.
CommentsPublished in Soft Computing (Section: Foundation, Algebraic, and Analytical Methods)
Journal refSoft Computing, 2026
DOI：10.1007/s00500-026-11413-9
