# Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing

arXiv: https://arxiv.org/abs/2210.06416 · HTML: https://mlsanity.com/paper/2210.06416/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [Decimeter Ranging with Channel State Information](https://mlsanity.com/paper/1902.09652/) (2019)

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

- [Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols](https://mlsanity.com/paper/2609.20765/) (2026)

## Abstract (arXiv)

The application of machine learning (ML) techniques in wireless communication domain has seen a tremendous growth over the years especially in the wireless sensing domain. However, the questions surrounding the ML model's inference reliability, and uncertainty associated with its predictions are never answered or communicated properly. This itself raises a lot of questions on the transparency of these ML systems. Developing ML systems with probabilistic modeling can solve this problem easily, where one can quantify uncertainty whether it is arising from the data (irreducible error or aleotoric uncertainty) or from the model itself (reducible or epistemic uncertainty). This paper describes the idea behind these types of uncertainty quantification in detail and uses a real example of WiFi channel state information (CSI) based sensing for motion/no-motion cases to demonstrate the uncertainty modeling. This work will serve as a template to model uncertainty in predictions not only for WiFi sensing but for most wireless sensing applications ranging from WiFi to millimeter wave radar based sensing that utilizes AI/ML models.