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 - #307449 |
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Title:
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Bayesian Visual Analytics: A Formal Visual Navigation Scheme
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Author(s):
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Scotland Leman*+
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Companies:
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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 ;
Adjustable Visualization ;
High Dimensional ;
PCA ;
Clustering
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Abstract:
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Massive data-sets contain information which is concentrated in very small regions of a very high dimensional space. In order to extract this information, it is necessary to draw on several different fields, with a variety of tools. We develop a new analytics framework, merging two areas of research, Bayesian Statistics and Visual Analytics, which we moniker: Bayesian Visual Analytics (BaVA). Mathematical and statistical disciplines rely on model based formulations, making use of structured parameterizations; whereas, simple visualization of high dimensional data has utility, but coherent organization of the display is often a difficulty. In this new paradigm, we synthesize these tools in order to make a cohesive, adjustable visualization, through user feedback. This talk will focus on presenting the BaVA proces, the formal descriptions of cognitive and parametric feedback, and examples.
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Authors who are presenting talks have a * after their name.
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