# A Unified Bayesian Framework for Modeling Measurement Error in Multinomial Data

arXiv: https://arxiv.org/abs/2310.09345 · HTML: https://mlsanity.com/paper/2310.09345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2023.

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

- [Tree-Based Predictive Models for Noisy Input Data](https://mlsanity.com/paper/2603.07409/) (2026)

## Abstract (arXiv)

Measurement error in multinomial data is a well-known and well-studied inferential problem that is encountered in many fields, including engineering, biomedical and omics research, ecology, finance, official statistics, and social sciences. Methods developed to accommodate measurement error in multinomial data are typically equipped to handle false negatives or false positives, but not both. We provide a unified framework for accommodating both forms of measurement error using a Bayesian hierarchical approach. We demonstrate the proposed method's performance on simulated data and apply it to acoustic bat monitoring and official crime data.