Relations among pattern studies?

[Torsten]

I definitely agree that there's a close connection between the patterns I was describing and the network you (and Schinner) have written about.  Still, I want to be careful when drawing conclusions about what's causing what.  You write here that the so-called "loop" properties are caused by the network (first paragraph), but then that the network is in turn caused by a "deep correlation between frequency, similarity, and spatial vicinity of tokens" (second paragraph).  Since I'd consider "deep correlation between frequency, similarity, and spatial vicinity of tokens" to be a reasonably good description of the system I was describing, those two paragraphs strike me as forming a little loop of their own.  So what came first, the chicken or the egg?

Indeed, I argue here that the network describes the text and not that the network is causing the text. Since "all pages containing at least some lines of text do have in common that
pairs of frequently used words with high mutual similarity appear. The exact cooccurrences may vary: there are pages where [daiin] is paired with [dain], but also pages where it is frequently used together with [aiin] (f41v, f46r, f55v, f89v2, v105v and f114r) or [saiin] (f2r, f16r, and f90r2)." [Timm & Schinner 2020, p. 3]. With other words if you write for whatever reason for every two instance of [daiin] also an instance of [aiin] this must result in an observable correlation.

However, my point is that the properties of glyph-by-glyph transitional probabilities mirror those of the network of similar words. Within this network, alongside [daiin], we also find words like [dain], [aiin], and [saiin]. Therefore, not only does [daiin] contribute to the transitional probability for the sequence [da], but all other words containing [da] do as well. Similarly, all tokens containing [ai] contribute to the transitional probability for a sequence where [a] is followed by [i]. Since the general principle in this network of similar words is that high-frequency tokens tend to have a greater number of similar words, it is unsurprising that the most likely paths in the transitional probability matrix result in frequently used words.

On the one hand, we have a network of similar words whose frequency correlates remarkably well with their degree of similarity to a few specific models.

On the other hand, we have a set of generative rules (involving glyph-by-glyph transitional probabilities) that would produce approximately that same set of words with approximately the same frequencies.  Other models (e.g., some "word paradigms") may be able to do the same.

To construct the glyph-by-glyph transitional probability tables, it is necessary to count the frequency with which certain glyphs follow one another. These tables are thus derived directly from the text. However, given that the Voynich text exhibits variation, it would be essential to use different transitional probabilities for different pages/sections of the manuscript. If one assumes that a device was used to generate the text, then multiple devices would be required to account for these variations.

If on the other hand someone is copying words with minor changes, they only need pattern recognition to identify words and a bit of creativity to modify them. No manual is required to explain how to use one's eyes to select a source word or how to employ creativity to alter it. Since the generated words will inherently share similarities, and because the process is recursive, this approach would naturally produce a text with statistical features resembling those found in the Voynich manuscript.
However, since words are mostly copied from the same page, the text would vary from page to page. On one page, the scribe might start with an instance of [daiin] and modify it into [dain], then copy [dain] into [daiir], and later change [daiir] back into [daiin]. On another page, the scribe might also start with [daiin] but modify it into [saiin], then change [daiin] into [dain], then use [saiin] again and modify it into [dain], and finally [dain] back into [daiin]. Even if the starting point is the same, the outcome would differ, resulting in variation from folio to folio. 
At the same time, the scribe might introduce new spelling variants. For example, he could decide to add [aiin] alongside [daiin]. This change would affect only the text generated after [aiin] was introduced, leading to observable developments in the manuscript. 
Decide for yourself whether the patterns observed in the Voynich text align with this description.