# An Information Theoretic Treatment of Yager's Probability Distribution Negation

arXiv: https://arxiv.org/abs/2608.00594 · HTML: https://mlsanity.com/paper/2608.00594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 1 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Learning from Complementary Labels](https://mlsanity.com/paper/1705.07541/) (2017)
- [Exponential Negation of a Probability Distribution](https://mlsanity.com/paper/2010.11533/) (2020)
- [Generating Negations of Probability Distributions](https://mlsanity.com/paper/2103.14986/) (2021)
- [Contracting and Involutive Negations of Probability Distributions](https://mlsanity.com/paper/2103.16176/) (2021)

## Abstract (arXiv)

In the seminal paper (Yager 2015), Yager defined the negation of a probability distribution p=(p1,dots,pn), as the distribution overlinep = (overlinep1,dots,overlinepn), where overlinepi = (1-pi)/(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.