Inference for unreliable grading: The case of recommendation letters

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 to the score based on the available information. In...

Full description

Saved in:
Bibliographic Details
Main Authors: Davis, Sergio, Loyola, Claudia, Peralta, Joaquín
Format: Conference Proceeding
Language:English
Subjects:
Bayesian analysis
Maximum entropy
Statistical inference
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 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.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0133195