# Estimating prevalence with precision and accuracy

arXiv: https://arxiv.org/abs/2507.06061 · HTML: https://mlsanity.com/paper/2507.06061/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Classifier Calibration: A survey on how to assess and improve predicted class probabilities](https://mlsanity.com/paper/2112.10327/) (2021)
- [Quantification under prior probability shift: the ratio estimator and its extensions](https://mlsanity.com/paper/1807.03929/) (2018)
- [QuaPy: A Python-Based Framework for Quantification](https://mlsanity.com/paper/2106.11057/) (2021)
- [A Comparative Evaluation of Quantification Methods](https://mlsanity.com/paper/2103.03223/) (2021)
- [Kernel Density Estimation for Multiclass Quantification](https://mlsanity.com/paper/2401.00490/) (2023)
- [Confidence intervals for class prevalences under prior probability shift](https://mlsanity.com/paper/1906.04119/) (2019)
- [Minimising quantifier variance under prior probability shift](https://mlsanity.com/paper/2107.08209/) (2021)
- [Bayesian Quantification with Black-Box Estimators](https://mlsanity.com/paper/2302.09159/) (2023)

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

- [Geometry-Aware Bayesian Quantification via Compositional Data Analysis](https://mlsanity.com/paper/2607.04977/) (2026)

## Abstract (arXiv)

Unlike classification, whose goal is to estimate the class of each data point, quantification (or prevalence estimation) aims to estimate the distribution of classes in a dataset. An important task in prevalence estimation is to quantify the uncertainty in prevalence estimates. In this paper, we introduce Precise Quantifier (PQ), a Bayesian aggregative quantifier that achieves narrow prediction intervals with sufficient coverage (i.e., sufficient proportion of intervals containing the true prevalence). We find that PQ produces more precise prevalence estimates than existing methods as the discriminative power of the underlying classifier increases and as the validation-to-test size ratio increases. These empirical results suggest that PQ uses validation information more effectively to quantify uncertainty in prevalence estimates than existing approaches.