Vord paradigm tool

[Torsten]

Torsten's [ol], [chedy], and [daiin] prototypes correspond rather closely to closed loops [cholcholchol...], [qokeedyqokeedyqokeedy...], and [daiindaiindaiin...], made up of the highest-probability sequences of individual graphemes in particular parts of the manuscript, such that if transitional probability matrices have high predictive power in themselves, we should also expect to see word frequencies correlate with "edit distance" as Torsten has found -- though not exactly, since the words [ol] and [chedy] will often represent partial cycles in contexts such as [~r.ol~] and [~l.chedy~].  So I think it might be possible to predict the "network" patterns on the basis of transitional probability matrices, with no need to factor in "edit distance" as such.  Perhaps we could think of a statistical test that would yield a different result depending on which model is more effective.

The reason that the words frequencies correlate is that "all pages containing at least some lines of text do have in common that pairs of frequently used words with high mutual similarity appear" [Timm & Schinner 2019, p. 3]. This means it is also possible to predict the occurrence of a words. A good example for this observation is the change from Currier A to Currier B, or the shift from <chol> dominated text to <chedy>-dominated text (see You are not allowed to view links. Register or Login to view.) e.g. that if <chedy> is used more frequently, this also increases the frequency of similar words, like <shedy> or <qokeedy>. In your blog post "Rightward and Downward in the Voynich Manuscript" you also describe some examples for the effect that the occurrence of a glyph like <sh> or <ch> increases the tendency towards another occurrence. This way the word frequencies confirm that a deep correlation between similar words exists (see also my examples given in this thread You are not allowed to view links. Register or Login to view.).