See You at the Posterior Line: Learning Bayesian Modeling Through a Car Racing Game
Abstract
We present an interactive classroom activity designed to address a central challenge in teaching introductory Bayesian statistics: how to formalize subjective knowledge and available information into prior distributions and then update them with empirical data. Role-playing as data analysts for a racing team, students evaluate candidate tires by converting qualitative engineering reports into prior distributions, collecting primary data via a virtual racing game, and using a Beta-Binomial model to inform team strategy. This discovery-based exercise allows small groups to observe directly how different prior choices and sample data jointly shape posterior inference. Student feedback (n=32) highlights high enjoyment, engagement and improved conceptual clarity. Open-access materials to implement the activity are provided, alongside recommendations for adapting it to other teaching contexts.
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BibTeX
@misc{ricci2026posterior,
title = {{See You at the Posterior Line: Learning Bayesian Modeling Through a Car Racing Game}},
author = {Federica Zoe Ricci and Mine Dogucu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.23166},
eprint = {2609.23166},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.23166},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - See You at the Posterior Line: Learning Bayesian Modeling Through a Car Racing Game
AU - Federica Zoe Ricci
AU - Mine Dogucu
PY - 2026
DA - 2026/09/19
PB - arXiv
DO - 10.48550/arxiv.2609.23166
UR - https://arxiv.org/abs/2609.23166
AB - We present an interactive classroom activity designed to address a central challenge in teaching introductory Bayesian statistics: how to formalize subjective knowledge and available information into prior distributions and then update them with empirical data. Role-playing as data analysts for a racing team, students evaluate candidate tires by converting qualitative engineering reports into prior distributions, collecting primary data via a virtual racing game, and using a Beta-Binomial model to inform team strategy. This discovery-based exercise allows small groups to observe directly how different prior choices and sample data jointly shape posterior inference. Student feedback (n=32) highlights high enjoyment, engagement and improved conceptual clarity. Open-access materials to implement the activity are provided, alongside recommendations for adapting it to other teaching contexts.
ER -