# Jeffrey's update rule as a minimizer of Kullback-Leibler divergence

arXiv: https://arxiv.org/abs/2502.15504 · HTML: https://mlsanity.com/paper/2502.15504/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Learning from What's Right and Learning from What's Wrong](https://mlsanity.com/paper/2112.14045/) (2021)

## Built on by (most cited first, 1 of 1)

- [On Lockean beliefs that are deductively closed and minimal change](https://mlsanity.com/paper/2507.06042/) (2025)

## Abstract (arXiv)

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.