# A Note on Posterior Probability Estimation for Classifiers

arXiv: https://arxiv.org/abs/1909.05894 · HTML: https://mlsanity.com/paper/1909.05894/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

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

- [Optimal Linear Combination of Classifiers](https://mlsanity.com/paper/2103.01109/) (2021)

## Abstract (arXiv)

One of the central themes in the classification task is the estimation of class posterior probability at a new point bfx. The vast majority of classifiers output a score for bfx, which is monotonically related to the posterior probability via an unknown relationship. There are many attempts in the literature to estimate this latter relationship. Here, we provide a way to estimate the posterior probability without resorting to using classification scores. Instead, we vary the prior probabilities of classes in order to derive the ratio of pdf's at point bfx, which is directly used to determine class posterior probabilities. We consider here the binary classification problem.