Inference for unreliable grading: The case of recommendation letters

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work, a well defined procedure to assign a probability distribution to a score is presented. By considering a score 0 ≤ t ≤ 1 and using Bayesian inference together with Jaynes' Maximum Entropy Principle, we are able to assign an estimation <t> to the score based on the available information. In order to correctly define a score t, we assume a resolution Δt that enables us to assign a a score t∗ so that t∗ -Δt/2 ≤ t ≤ t∗ + Δt/2 with a confidence p, and infer the parameters of the maximum entropy distribution as a function of p and t∗. This framework may provide insights on how to state problems with uncertain evaluation of performance in learning in several contexts.

Original languageEnglish
Title of host publicationIWoSP 2021 - International Workshop on Statistical Physics
EditorsBoris Atenas, Francisco A. Calderon, Sergio Curilef, Yuvineza Gomez-Leyton, Diego Gonzalez
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735444829
DOIs
Publication statusPublished - 15 May 2023
Event2021 International Workshop on Statistical Physics, IWoSP 2021 - Antofagasta, Chile
Duration: 1 Dec 20213 Dec 2021

Publication series

NameAIP Conference Proceedings
Volume2731
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2021 International Workshop on Statistical Physics, IWoSP 2021
Country/TerritoryChile
CityAntofagasta
Period1/12/213/12/21

ASJC Scopus subject areas

  • General Physics and Astronomy

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