Hornbeck, HaysnAlim, Usman2021-06-112021-06-112019-10Hornbeck, H., & Alim, U. (2019). UofC-Bayes: A Bayesian approach to visualizing uncertainty in radiation data. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). doi:10.1109/vast47406.2019.8986936http://hdl.handle.net/1880/113482https://doi.org/10.11575/PRISM/46129Disasters demand a quick response based on incomplete information. For the Saint Himark dataset, part of the 2019 VAST Challenge, we focused on delivering a visualization which accurately conveyed that uncertainty. While our analysis was done offline, we chose techniques and algorithms which could easily be applied to real-time usage. Our visualization for the second mini-challenge was two separate screens for two separate tasks: a broad overview of radiation levels, and a detailed look at specific sensors.“© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”conference posterRGPIN-2019-05303http://dx.doi.org/10.1109/VAST47406.2019.8986936