After nearly 11 years, I’ve finally decided to pull the plug on Usenet archive:

The page you have requested no longer exists!

But don’t panic just yet!

I’ve coded this page in a way, that it’s monitoring each redirect & capturing data about the thread you’ve requested. Page you’ve requested is still available in Google’s Usenet Archive. You should be able to access it using following URL:

Zero mean normalized cross correlation

Note: I can’t guarantee that all pages can be found in Google Groups, but over 90% of the content should be there. If not, try the resources below!

Cross-correlation! – first normalized. This is typically done at every step by subtracting the mean and dividing by the standard deviation. That is, the cross-correlation of
Autocorrelation! – Autocorrelation, also known as serial correlation, is the cross-correlation of a signal with itself. Informally, it is the similarity between observations
Weighted arithmetic mean! – weights are normalized such that they sum up to , i.e. . For such normalized weights the weighted mean is simply . Note that one can always normalize the weights
Index of dispersion! – coefficient of dispersion, or variance-to-mean ratio (VMR), like the coefficient of variation, is a normalized measure of the dispersion of a probability
Least mean squares filter! – least mean squares filter is related to the Wiener filter, but minimizing the error criterion of the former does not rely on cross-correlations or auto-correlations

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