Data analysis techniques: Difference between revisions
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The goal of integrated data analysis is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement. | The goal of integrated data analysis is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement. | ||
* [[Function parametrization]] | * [[Function parametrization]] | ||
* Bayesian data analysis | * [[Bayesian data analysis]] | ||
== References == | == References == | ||
<references /> | <references /> | ||
Revision as of 15:34, 9 February 2010
This page collects information on data analysis techniques used in fusion research.
Temporal analysis
Linear analysis
- Correlation
- Fourier analysis
- Wavelet analysis
- Conditional analysis
- Probability distributions
- Rescaled range or Hurst analysis; Structure functions
Non-linear analysis
- Bicoherence analysis
- Chaos analysis (attractors, fractional dimensions)
- Hilbert-Huang transform
Spatial analysis
Most of the techniques listed under 'temporal analysis' can of course be applied to spatial data.
- Tomography (cf. TJ-II:Tomography)
Spatio-temporal analysis
Image analysis
- Twodimensional Fourier analysis
- Twodimensional wavelet analysis
- Event detection using thresholding
- Optical flow (for movies)
Integrated data analysis
The goal of integrated data analysis is to combine the information from a set of diagnostics providing complementary information in order to recover the best possible reconstruction of the actual state of the system subjected to measurement.