Computer Science > Information Theory
[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
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.
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