This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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574
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Graphics
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| Abstract - #307605 |
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Title:
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Learning with Dynamic Visualizations
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Author(s):
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Leanna House*+ and Scotland Leman
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Companies:
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Virginia Tech and Virginia Tech
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Address:
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Dept. of Statistics, Blacksburg, VA, 24061, United States
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Keywords:
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Bayesian ;
Visual Analytics ;
Learning ;
Visualizations ;
High Dimensions
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Abstract:
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In a previous paper, Leman and House (2010), we introduced Bayesian Visual Analytics (BaVA) and developed a procedure to create sensible and malleable displays of high dimensional data. Although, BaVA is more than a procedure, but a paradigm. In this talk, we will consider the statistical and visual analytic methods that we developed previously to be components to an overall analysis approach that relies on intuition, expert judgment, and dynamic tools - BaVA visualizations. We will explore some high dimensional applications where the dynamic visualization enables users to navigate the data and learn/discover structure easily and, arguably, more quickly than with traditional analytical methods.
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