article

Bayesian visual analytics: BaVA

Authors:

Scotland Leman,

Chao HanAuthors Info & Claims

Statistical Analysis and Data Mining, Volume 8, Issue 1

Pages 1 - 13

https://doi.org/10.1002/sam.11253

Published: 01 February 2015 Publication History

Abstract

Leman et al. and Endert et al. develop an interactive data visualization framework called visual to parametric interaction V2PI. With V2PI, experts may explore data visually assess multiple data visualizations based on their judgments and an underlying data analytic method. Specifically, V2PI offers a deterministic procedure to quantify expert judgments and update analytical parameters to create new data visualizations. In this article, we explain V2PI from a probabilistic perspective and develop Bayesian visual analytics BaVA. We model data probabilistically, develop parallels between quantifying expert judgments and eliciting prior distributions from experts, and justify how we update parameters using Bayesian sequential updating. We apply BaVA using two linear projections methods to assess simulated and real-world datasets.

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Cited By

Wenskovitch JDowling MNorth C(2023)Toward Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual AnalyticsACM Transactions on Interactive Intelligent Systems10.1145/358856514:1(1-35)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1145/3588565
Keith Norambuena BMitra TNorth C(2023)Mixed Multi-Model Semantic Interaction for Graph-based Narrative VisualizationsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584076(866-888)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584076
Han HFaust RKeith Norambuena BLin JLi SNorth C(2023)Explainable interactive projections of imagesMachine Vision and Applications10.1007/s00138-023-01452-934:6Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1007/s00138-023-01452-9
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cover image Statistical Analysis and Data Mining

Statistical Analysis and Data Mining Volume 8, Issue 1

February 2015

63 pages

ISSN:1932-1864

EISSN:1932-1872

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 February 2015

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Cited By

Wenskovitch JDowling MNorth C(2023)Toward Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual AnalyticsACM Transactions on Interactive Intelligent Systems10.1145/358856514:1(1-35)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1145/3588565
Keith Norambuena BMitra TNorth C(2023)Mixed Multi-Model Semantic Interaction for Graph-based Narrative VisualizationsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584076(866-888)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584076
Han HFaust RKeith Norambuena BLin JLi SNorth C(2023)Explainable interactive projections of imagesMachine Vision and Applications10.1007/s00138-023-01452-934:6Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1007/s00138-023-01452-9
Han HFaust RNorambuena BPrabhu RSmith TLi SNorth C(2022)Explainable Interactive Projections for Image DataAdvances in Visual Computing10.1007/978-3-031-20713-6_6(77-90)Online publication date: 3-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20713-6_6
Bian YNorth CHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)DeepSI: Interactive Deep Learning for Semantic InteractionProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450670(197-207)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450670
Wenskovitch JDowling MNorth CPaternò FOliver NConati CSpano LTintarev N(2020)With respect to what?Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377516(177-188)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377516
González Martínez AWooton BKirshenbaum NKobayashi DLeigh J(2020)Exploring Collections of research publications with Human Steerable AIPractice and Experience in Advanced Research Computing 2020: Catch the Wave10.1145/3311790.3396646(339-348)Online publication date: 26-Jul-2020
https://dl.acm.org/doi/10.1145/3311790.3396646
Correia ALecue F(2019)Human-in-the-loop feature selectionProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33012438(2438-2445)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33012438
Dowling MWycoff NMayer BWenskovitch JLeman SHouse LPolys NNorth CHauck P(2019)Interactive Visual Analytics for Sensemaking with Big TextBig Data Research10.1016/j.bdr.2019.04.00316:C(49-58)Online publication date: 1-Jul-2019
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Chuprikova EMacEachren ACron JMeng LLawonn KSmit NLinsen LKosara R(2018)Visual analytics-enabled Bayesian network approach to reasoning about public camera dataProceedings of the EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization10.5555/3290680.3290682(1-5)Online publication date: 4-Jun-2018
https://dl.acm.org/doi/10.5555/3290680.3290682
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