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Fix MIGHT test to use coleman method (but randomizing permutations per tree)
Add script for pulling data from public repository for real data analysis
Prove that a MIGHT statistic (e.g., S@98) from Variable Set 1 can be shown to be significantly different from the same statistic from Variable Set 2, even though the dimension of Variable Set 1 is far different from the number of dimensions in Variable Set 2 (need by the time we receive reviews from Science).
Run dimension power curves for Figure 1 and Supplement for smaller sample size
The text was updated successfully, but these errors were encountered:
The general MVN approach maybe can be done as Jovo suggested (w/ some open questions):
X_i | Y ~ MVN, where for CoMIGHT, we generate two such instances that are either directly dependent or not.
Y = mixture of MVN Gaussians, so the MI terms is then: $I(X1, X2; Y) = H(X1, X2) - H(X1, X2 | Y) = H(X1 | X2) + H(X2) - H(X1 | X2, Y) + H(X2 | Y)$
where the non-trivial parts to currently compute are:
H(X1 | X2) is unsure how to compute analytically, unless we numerically integrate?...
H(X1 | X2, Y) is the same
Maybe we generate a huge MVN first where we know the $\Sigma_{X1, X2}$ for the subset of variables we denote X1, X2, which is still MVN, and therefore we know H(X1, X2). Then, we use Y as the mixture of Gaussians w/ varying mixture probability?
The text was updated successfully, but these errors were encountered: