08-09-2020, 01:16 AM
(This post was last modified: 08-09-2020, 01:23 AM by DonaldFisk.)
(08-09-2020, 12:43 AM)Stephen Carlson wrote: You are not allowed to view links. Register or Login to view.(07-09-2020, 11:47 PM)DonaldFisk wrote: You are not allowed to view links. Register or Login to view.I generated some You are not allowed to view links. Register or Login to view. which You are not allowed to view links. Register or Login to view.. (So did Gordon Rugg and Torsten Timm, by different methods.)Right, there is an article by Wentian Li, You are not allowed to view links. Register or Login to view. (1992), that makes a similar claim. But the literature is not unanimous; for example, this article by Ramon Ferrer-i-Cancho and Brita Elvevåg, You are not allowed to view links. Register or Login to view. (2010), argues the opposite.
I was aware of Zipf's Law, and that it was followed by the Voynich Manuscript, at the time I generated my fake Voynich Manuscript, but following Zipf's Law was an accidental output, not an input. All my method required was a set of tables for choosing the current glyph given the previous glyph, and a method for generating random numbers. My method wasn't quite correct but it was enough to follow Zipf's Law, and that wasn't intentional (though I would have rejected it and tried something else if it didn't).
I don't really have to the time to referee this dispute, but as fair as I can tell there are plenty of cases were non-linguistic processes can generate Zipfian distributions. The upshot is, to use the terminology of biostatistics, the observation of a Zipfian distribution is sensitive to, but not specific of, a linguistic generating process. In other words, while the false negatives may be low, the false positives could be high.
ETA: I should I add that while I found these articles by a quick Google search, they are also cited in the Schinner & Timm paper.
There's an important difference. In my method, the probability of the current glyph depends on the previous glyph, so while the input is random, the output isn't completely random. I think all alphabetic languages have that property and suspect it's enough to make the distribution Zipfian. It would be nice to have a mathematical proof though, or failing that, an experimental verification.


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