# Posterior Adaptation With New Priors

arXiv: https://arxiv.org/abs/2007.01386 · HTML: https://mlsanity.com/paper/2007.01386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Confident AI](https://mlsanity.com/paper/2202.05957/) (2022)
- [Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm](https://mlsanity.com/paper/2401.09376/) (2024)

## Abstract (arXiv)

Classification approaches based on the direct estimation and analysis of posterior probabilities will degrade if the original class priors begin to change. We prove that a unique (up to scale) solution is possible to recover the data likelihoods for a test example from its original class posteriors and dataset priors. Given the recovered likelihoods and a set of new priors, the posteriors can be re-computed using Bayes' Rule to reflect the influence of the new priors. The method is simple to compute and allows a dynamic update of the original posteriors.