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Computer Science > Information Theory

arXiv:2510.04314v1 (cs)
[Submitted on 5 Oct 2025 ]

Title: Relative Divergence and Maximum Relative Divergence Principle for Grading Functions on Partially Ordered Sets

Title: 相对分歧与部分有序集上评分函数的相对最大分歧原理

Authors:Alexander Dukhovny
Abstract: Relative Divergence (RD) and Maximum Relative Divergence Principle (MRDP) for grading (order-comonotonic) functions (GF) on posets are used as an expression of Insufficient Reason Principle under the given prior information (IRP+). Classic Probability Theory formulas are presented as IRP+ solutions of MRDP problems on conjoined posets. RD definition principles are analyzed in relation to the poset structure. MRDP techniques are presented for standard posets: power sets, direct products of chains, etc. "Population group-testing" and "Single server of multiple queues" applications are stated and analyzed as "IRP+ by MRDP" problems on conjoined base posets.
Abstract: 相对发散性(RD)和最大相对发散性原理(MRDP)用于在偏序集上对分级(顺序同单调)函数(GF)进行评分,作为在给定先验信息下的不足理由原理的表达(IRP+)。 经典概率论公式被呈现为在结合偏序集上的MRDP问题的IRP+解。 相对发散性定义原则与偏序集结构的关系进行了分析。 针对标准偏序集(如幂集、链的直积等)介绍了MRDP技术。 “群体检测”和 “多个队列的单服务台”应用被陈述并分析为在结合基本偏序集上的“IRP+ by MRDP”问题。
Comments: 14 pages
Subjects: Information Theory (cs.IT)
MSC classes: Primary 94, Secondary 90
Cite as: arXiv:2510.04314 [cs.IT]
  (or arXiv:2510.04314v1 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.2510.04314
arXiv-issued DOI via DataCite

Submission history

From: Alexander Dukhovny [view email]
[v1] Sun, 5 Oct 2025 18:24:13 UTC (13 KB)
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