Hoax theory discovery by running lang analysis program according to my methodology

[maskci]

•) "The key is creating a couple databases of text samples"
   --Please do so , afaik there are few of these, OCR some relevant texts, correct them , decide what to do about abbreviations if any, etc.

•) "High probability transformation methods (possibly low effort to optimize energy expenditure)"
   --How is this to be quantified?,  what algorithms should / could be used?
  --If by "energy expenditure" you mean biophysical energy then how do we answer the question 'how many Calories does it take to write daiin with a quill pen?'

•) "We could run a sort of a preliminary, take some key things the source database has,"
 --'sort of',  'some key things'  these terms are not very helpful, please specify.

•) "produce something closer to Voynichese."
  --by what metric?  There are many statistics for Voynichese, which ones do you suggest ?

Hopefully  you can add some more detail to your method for it to be followed.

I will think about a properly detailed plan soon, but here are some specifics about what I already thought of. 

1. The first stage would be to understand texts scribes had access to, sources of inspiration, collect by country, gather the data, clean it up. Use this as per country, add some simulation if we need more data points of course, or date back known texts based on reference.

The original database should contain bifolia, and work based on that presumption.

2. The second stage would involve working with this database, by running entropy and NLP tests for these texts, on a bifolia basis, they get processed 1 by 1, but under the category of 1 book by book
A. Two variables, remove all words shorter than x, longer than y, run it until exhaustion and curves are produced.
B. First Letter Remains, Last Letter Remains, Length based with variable deviation
C. A+B runtime, get the curves.

3. The third stage would be to take the top cases produced by point 2 and run them against the VM.
Do it sequentially: a bifolio of VM [bound to] bifolio of source book, analysis. Use all possible datasets of the books running them by VM bifolia, to try to find a glyphizing matrix.

This way we get a set of possibilities, 3.1. glyph per word unprocessed, 3.2. Glyph per word processed, 3.3. glyph per characters, and 3.4. Glyph per character separately.

Common combo in VM - try to match it similarly treating these combos as glyphs. 

Just getting the best results and trying to find even singular rules. Where something matches close to perfectly.

There are closest matches in this methodology and they prove or disprove this theory. 
Then it is about tweaking the parameters, maybe change how they read, top to bottom, right to left, think of more ways what was easiest to them, how they divided labor.

Example of a small breakthrough: VM pages type scribe 1 type dialect A, >against eg. Specific Latin book pages 40-42, in variant 2.B.2.<, yield 50 counts of character Z matching against 49 counts in the Source Book, 40 counts of combo Y, matching 40 patterns in Source book, 20 counts of combo X... and so on.