# The Principle of Uncertain Maximum Entropy

arXiv: https://arxiv.org/abs/2305.09868 · HTML: https://mlsanity.com/paper/2305.09868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 2 of 2)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Branch-and-Cut Algorithm for Mixed Integer Bilevel Linear Optimization Problems and Its Implementation](https://mlsanity.com/paper/2104.09010/) (2021)

## Abstract (arXiv)

The Principle of Maximum Entropy is a rigorous technique for estimating an unknown distribution given partial information while simultaneously minimizing bias. However, an important requirement for applying the principle is that the available information be provided error-free (Jaynes, 1982). We relax this requirement using a memoryless communication channel as a framework to derive a new, more general principle. We show our new principle provides an upper bound on the entropy of the unknown distribution and the amount of information lost due to the use of a given communications channel is unknown unless the unknown distribution's entropy is also known. Using our new principle we provide a new interpretation of the classic principle and experimentally show its performance relative to the classic principle and some other generally applicable solutions.