Jeffrey's update rule as a minimizer of Kullback-Leibler divergence
Machine Learning
2025-02-24 v1 Cryptography and Security
Abstract
In this paper, we show a more concise and high level proof than the original one, derived by researcher Bart Jacobs, for the following theorem: in the context of Bayesian update rules for learning or updating internal states that produce predictions, the relative entropy between the observations and the predictions is reduced when applying Jeffrey's update rule to update the internal state.
Keywords
Cite
@article{arxiv.2502.15504,
title = {Jeffrey's update rule as a minimizer of Kullback-Leibler divergence},
author = {Carlos Pinzón and Catuscia Palamidessi},
journal= {arXiv preprint arXiv:2502.15504},
year = {2025}
}