deletions | additions       diff --git a/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex b/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex  index 890d19d..88dea54 100644  --- a/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex  +++ b/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex 
...
\subsubsection{Bispectrum/Bicoherence}  The Bispectrum measures the magnitude and phase correlation between (Fourier) signals (in Fourier Space...one of two). It is a three-point statistic obtained by taking the Fourier Transform of a three-point correlation function, as described in Koch et al. (2015), and is usually complex. In order to better quantify differences between our runs, we use the Bispectrum to calculate the Bicoherence, a real-valued, normalized equivalent. To calculate and interpret the Bispectrum and Bicoherence for runs W1T2t0.2 and W2T2t0, we closely follow the analysis of Koch et al. (2015). For both of our integrated intensity maps, we compute the Bicoherence using two sets of randomly sampled spatial frequencies up to half of our image size. Figure 8 depicts the Bicoherences Bicoherence matrices  for runs W1T2t0.2 and W2T2t0 W2T2t0. Run W1T2t0.2's matrix contains many (reword) Bicoherences near zero, while run W2T2t0's matrix contains many Bicoherences grater than 0.5. Similar to PCA, we find that feedback  (also hard to work without plagiarizing)