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Mathematikon, Lecture Hall <br> | Mathematikon, Lecture Hall <br> | ||
<font size="1">(*University of North Carolina) </font> | <font size="1">(*University of North Carolina) </font> | ||
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| + | The rapid change in computational capabilities had made Big Data a major modern statistical challenge. Less well understood is the rise of Complex Data as a perhaps greater challenge. Object Oriented Data Analysis (OODA) is a framework for addressing this, in particular providing a general approach to the definition, representation, visualization and analysis of Complex Data. The notion of OODA generally guides data analysis, trough providing a useful terminology for interdisciplinary discussion of the many choices typically needed in modern complex data analyses. The main ideas are illustrated via a survey of a number of approaches which integrate differential geometry and Bayesian statistics, yielding powerful image segmentations. | ||
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Revision as of 12:54, 7 June 2019
Workshop
Geometry, Topology, and Computation
June 12 - 14
Heidelberg, Mathematikon
Organizers: Peter Albers, Roman Sauer, Anna Wienhard
website
Stochastics Colloquium
Object Oriented Data Analysis - Steve Marron*
June 14, 15.00-16.00
Mathematikon, Lecture Hall
(*University of North Carolina)
Abstract: The rapid change in computational capabilities had made Big Data a major modern statistical challenge. Less well understood is the rise of Complex Data as a perhaps greater challenge. Object Oriented Data Analysis (OODA) is a framework for addressing this, in particular providing a general approach to the definition, representation, visualization and analysis of Complex Data. The notion of OODA generally guides data analysis, trough providing a useful terminology for interdisciplinary discussion of the many choices typically needed in modern complex data analyses. The main ideas are illustrated via a survey of a number of approaches which integrate differential geometry and Bayesian statistics, yielding powerful image segmentations.