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  2. Bicoherence - Wikipedia

    en.wikipedia.org/wiki/Bicoherence

    The Fourier transform of the second-order cumulant, i.e., the autocorrelation function, is the traditional power spectrum. The Fourier transform of C 3 (t 1,t 2) (third-order cumulant) is called bispectrum or bispectral density. They fall in the category of Higher Order Spectra, or Polyspectra and provide supplementary information to the power ...

  3. Autocorrelation - Wikipedia

    en.wikipedia.org/wiki/Autocorrelation

    The (potentially time-dependent) autocorrelation matrix (also called second moment) of a (potentially time-dependent) random vector = (, …,) is an matrix containing as elements the autocorrelations of all pairs of elements of the random vector .

  4. Higher order coherence - Wikipedia

    en.wikipedia.org/wiki/Higher_order_coherence

    Higher order coherence or n-th order coherence (for any positive integer n>1) extends the concept of coherence to quantum optics and coincidence experiments. [1] It is used to differentiate between optics experiments that require a quantum mechanical description from those for which classical fields suffice.

  5. Self-Similarity of Network Data Analysis - Wikipedia

    en.wikipedia.org/wiki/Self-Similarity_of_Network...

    Suppose be a weakly stationary (2nd-order stationary) process with mean , variance , and autocorrelation function ().Assume that the autocorrelation function () has the form () as , where < < and () is a slowly varying function at infinity, that is () = for all >.

  6. Cross-correlation - Wikipedia

    en.wikipedia.org/wiki/Cross-correlation

    For jointly wide-sense stationary stochastic processes, the definition is = ⁡ = ⁡ [() (+) ¯] The normalization is important both because the interpretation of the autocorrelation as a correlation provides a scale-free measure of the strength of statistical dependence, and because the normalization has an effect on the statistical ...

  7. Lyapunov exponent - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_exponent

    In 1930 O. Perron constructed an example of a second-order system, where the first approximation has negative Lyapunov exponents along a zero solution of the original system but, at the same time, this zero solution of the original nonlinear system is Lyapunov unstable. Furthermore, in a certain neighborhood of this zero solution almost all ...