Voynich words evolve over a linear sequence

[npcompl33t]

I think you probably found the most stringent constraints any viable Voynich theory must respect (btw, I really hope for a peer-reviewed article). Great job!

So, I really hope your results hold (I think so, but I'm no expert in statistics). Let me sum them up, as a check of my own understanding.

• You confirm the basic unit of the VMS is the bifolio (1).
  
• You found and retrieved a gradient in the distributions of bigrams in each bifolio (2) (3)
  
• And now you have determined a rather small set of textual 'transition rules' which smoothly transform the bigrams distribution of a bifolio into the next (you actually said 'the words' in this thread but I though you were using bigrams P(b|a) distributions? can you clarify this to me please?). Which, taken face value, is a great result.
  

I'd really like to know more about the 'transition rules' and as @Marco P, I'd really appreciate a post here summing the transition rules (and their statistical relevance). I'm sorry but digging in a complex website to get the informations out is not so easy (4)

Yes, this analysis is entirely different from the P(b|a) distributions. I basically took the sequence that analysis uncovered, then started looking at actual words to attempt understand how the abstract bigram changes manifest at the word level. This shows that word level turnover aligns with the chronology analysis, and it presents a set of rules that explain much of the shift among common words.

However, I don't want to overstate the 'transition rules'. I am not claiming my rules smoothly translate all words across all steps. I have found evidence that a small set of rules can cover the changes across most of the more frequent words, but they do not yet fully explain every shift in the bigram distribution. My work up until now required matching words between sections based on frequency which is only possible with common words.

I am now trying to expand my work to be able to cover rare word and hapax transformations. There does appear to be some differences in how these changes affect common vs rare words. I think this is compatible with both self-citation and semantic preserving drift, resolving that is going to take more work.

As a community, I think the most pressing things to resolve are:

• A more comprehensive set of word transformations
  
• A better understanding of these changes across each 'cluster' and bifolio, especially the bifolios in the middle of the sequence as the ordering there is more stable than the end sections
  
• how the transformations propagate between rare and common words
  
• what words appear to be affected by what changes
  
• whether the transformations appear to preserve information or semantic meaning
  

I am working on expanding this work to dig into these but I am not claiming to have already answered all of the above questions.