Data analysis techniques: Difference between revisions

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* Conditional analysis
* Conditional analysis
* Probability distributions
* Probability distributions
* [[:Wikipedia:Rescaled range|Rescaled range]] or Hurst analysis; Structure functions


=== Non-linear analysis ===
=== Non-linear analysis ===


* [[:Wikipedia:Bicoherence|Bicoherence]] analysis
* [[:Wikipedia:Bicoherence|Bicoherence]], bispectrum
* Chaos analysis ([[:Wikipedia:Attractor|Strange attractor]], [[:Wikipedia:Fractal dimension|Fractal dimension]], [[:Wikipedia:Mutual information|Mutual information]])
* Chaos analysis ([[:Wikipedia:Attractor|Strange attractor]], [[:Wikipedia:Fractal dimension|Fractal dimension]], [[:Wikipedia:Mutual information|Mutual information]], [[:Wikipedia:Lyapunov exponent|Lyapunov exponent]])
* [[:Wikipedia:Hilbert-Huang transform|Hilbert-Huang transform]]
* [[:Wikipedia:Hilbert-Huang transform|Hilbert-Huang transform]]
=== Self-similarity ===
* [[:Wikipedia:Rescaled range|Rescaled range]] or Hurst analysis; Structure functions
* Waiting times, quiet times <ref>[
http://link.aps.org/doi/10.1103/PhysRevE.66.036124 R. Sánchez et a., ''Quiet-time statistics: A tool to probe the dynamics of self-organized-criticality systems from within the strong overlapping regime'',  Phys. Rev. E '''66''', 036124 (2002)]</ref>


== Spatial analysis ==
== Spatial analysis ==
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[[Error propagation]]
[[Error propagation]]
== References ==
<references />

Revision as of 20:36, 13 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

Non-linear analysis

Self-similarity

Spatial analysis

Most of the techniques listed under 'temporal analysis' can of course be applied to spatial data.

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.

See also

Error propagation

References

  1. [ http://link.aps.org/doi/10.1103/PhysRevE.66.036124 R. Sánchez et a., Quiet-time statistics: A tool to probe the dynamics of self-organized-criticality systems from within the strong overlapping regime, Phys. Rev. E 66, 036124 (2002)]