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On the Interpolation of Data with Normally Distributed Uncertainty for Visualization.
S Schlegel1, N Korn, G Scheuermann
1University of Leipzig. schlegel@informatik.uni-leipzig.de
IEEE Transactions on Visualization and Computer Graphics
|September 11, 2015
Summary
Visualizing uncertain data, often Gaussian distributed, requires careful interpolation. This study shows standard linear methods are suboptimal, recommending geostatistics and machine learning techniques for better uncertainty visualization.
Area of Science:
- Data visualization
- Scientific computing
- Statistical modeling
Background:
- Uncertain data is prevalent across science and engineering.
- Gaussian distributions are commonly used to model data uncertainty.
- Interpolation is crucial for visualizing uncertain data on fixed positions.
Purpose of the Study:
- Analyze the impact of linear interpolation on visualizing Gaussian distributed uncertain data.
- Identify superior methods for uncertainty visualization.
Main Methods:
- Evaluation of standard linear interpolation schemes.
- Application of geostatistical methods.
- Utilization of machine learning techniques.
Main Results:
- Linear interpolation schemes can distort the visualization of Gaussian distributed uncertainty.
- Geostatistical and machine learning methods demonstrate favorable properties for uncertainty visualization.
Conclusions:
- Standard interpolation methods are not ideal for visualizing Gaussian uncertain data.
- Advanced techniques from geostatistics and machine learning offer improved solutions for uncertainty visualization.
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