UofC-Bayes: A Bayesian Approach to Visualizing Uncertainty in Radiation Data

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dc.contributor.authorHornbeck, Haysn
dc.contributor.authorAlim, Usman
dc.date.accessioned2021-06-11T21:39:51Z
dc.date.available2021-06-11T21:39:51Z
dc.date.issued2019-10
dc.description.abstractDisasters 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.en_US
dc.description.grantingagencyNatural Sciences and Engineering Research Council (NSERC)en_US
dc.identifier.citationHornbeck, 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/vast47406.2019.8986936en_US
dc.identifier.doihttp://dx.doi.org/10.1109/VAST47406.2019.8986936en_US
dc.identifier.grantnumberRGPIN-2019-05303en_US
dc.identifier.urihttp://hdl.handle.net/1880/113482
dc.publisher.departmentComputer Scienceen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen_US
dc.rights“© 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.”en_US
dc.titleUofC-Bayes: A Bayesian Approach to Visualizing Uncertainty in Radiation Dataen_US
dc.typeconference posteren_US
ucalgary.item.requestcopyfalseen_US
ucalgary.scholar.levelFaculty, Graduateen_US
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