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Main Author: Dukhovny, Alexander
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2510.04314
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author Dukhovny, Alexander
author_facet Dukhovny, Alexander
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2510_04314
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Relative Divergence and Maximum Relative Divergence Principle for Grading Functions on Partially Ordered Sets
Dukhovny, Alexander
Information Theory
Primary 94, Secondary 90
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.
title Relative Divergence and Maximum Relative Divergence Principle for Grading Functions on Partially Ordered Sets
topic Information Theory
Primary 94, Secondary 90
url https://arxiv.org/abs/2510.04314