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| Upcoming Voynich program at the Getty |
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Posted by: LisaFaginDavis - 19-05-2025, 01:55 PM - Forum: News
- Replies (15)
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Hi, everyone,
Sorry for my radio silence - my day job has kept me very busy (it isn't easy working in the humanities in the United States at the moment). I'm also working on three different interdisciplinary Voynich collaborations, implementing various methodologies to try and determine the original sequence of bifolia within the different sections of the manuscript.
I wanted to let you all know that I will be participating in an online program about the VMS sponsored by the Getty Museum in Los Angeles and hosted by curator Elizabeth Morrison, taking place on June 13 at 3 PM Eastern. I hope you will join us! Click here for more information and to register:
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- Lisa
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| An attempt at extracting grammar from vord order statistics. |
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Posted by: davidd - 19-05-2025, 03:55 AM - Forum: Analysis of the text
- Replies (90)
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Hi All,
Based on vords coming before and after, grouping vords into vordgroups looks possible.
The past few weeks i have been programming in python to do some analysis and statistical informed guessing on grammar in voynechese. I am happy to announce to you these partial/preliminary results. There seem to be statistically significant groupings of vords that have either increased or decreased likelyhood to either precede or follow certain other groupings of vords.
What i am looking for
I would like to make some academic paper out of these results. That is why i am looking for the help of any academic voynich researcher that would like to collaborate. These results look statistically significant to my amateur eyes but I still have to do some p-value calculations, I think Chi Square would be the appropiate one for this.
assumptions
+ A and B are different languages.
+ each vord is matching a word in a real language
+ the real language has some form of positional grammar, ex like some prepositions come with a genetive case associated directly following the preposition
+ vords have only one meaning and every time a vord is used it means only that one meaning (statistics will still work even if this one isnt true)
method:
language A and language B are processed seperately
for each vord besides frequency also tally preceding and following vords, respecting paragraphs and ignoring line breaks
for all vords that appear at least 4 times, put them in little vordgroups up to around 5 vords each based on similarity in vords coming before and after them.
now score all vordgroups against eachother and merge the most similar ones, not looking at each individual vord transition but transitions from vordgroup to vordgroup
merge until desired amount of vordgroups left
score each of the most frequent vords against all groups to see if any would fit better in another group.
safeguards:
By just looking at the more frequent vords there is less wiggle room than when assigning unique vords to some group to increase the score.
Because the non frequent vords were counted in the total for the percentage calculation, this makes total transition frequency to each of the labeled groups lower.
cons/doubts/possible improvements:
The algoritm I built is made to find these patterns. It has not been tested on random noise or other language samples.
It takes a lot of time, the merging step takes aroung one hour on my poor old pc.
there may be some bugs in the searching algorithm, It is not very stable, the groups that come out are different every time. Probably some memory in python that gives a different order every time. Maybe it is an omen that the method may be flawed.
The output is very long, but with analysing over 27000 vords that is somewhat inevitable. A big part of the output is guessing for all the non-frequent vords in which group they would fit best.
It really feels great to be standing on the shoulders of giants and looking further than anyone before.
Thanks to the members of the voynich ninja and the maintainers of websites about voynich.
Maybe this work can help provide a break through for somebody else.
the results:
statistics about language A and language B are in the same file. first all A output, than all B output
line number chapter
1 Language A
13 initial groups
99 merging
380 vordgroup stats
847 transition tables
894 moving vords to other groups
1217 guessing but not adding to groups of other vords
4466 vordgroup stats
5024 transition tables
5079 Language B
5087 initial groups
5233 merging
5775 vordgroup stats
6149 transition tables
6197 moving vords to other groups
6850 guessing but not adding to groups of other vords
11434 vordgroup stats
11930 transition tables
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Code: vordgroup 56: cheody 56 446
members: ['cheody', 'opar', 'shek', 'sheody', 'shody', 'she', 'opchedy', 'psheody', 'opchdy', 'cheky', 'tchy', 'chekaiin', 'ytedy', 'olkchedy', 'ytchey', 'ytody', 'cholky', 'chcphy', 'lkeey', 'ycheedy', 'shor', 'olky', 'sshey', 'shckhey', 'keol', 'teeody', 'shaiin', 'lkeeedy', 'ycheeo', 'cheoty', 'shekeey', 'chotal']
num members: 32
vord count: 446
groupname: cheody
lesser likely following : chedy 5.16% instead of 16.02%
more likely following : daiin 6.95% instead of 3.37%
lesser likely followed by : chedy 8.07% instead of 16.02%
more likely followed by : qokain 21.08% instead of 15.88%
coming from group <groupname> followed by <groupname> which has a relative size of <x>
---------------------------------------------------------
< other> -> 31.39% <cheody> 37.44% -> < other>
< chedy> -> 5.16% <cheody> 8.07% -> < chedy> rel size: 16.02%
< ol> -> 6.50% <cheody> 6.50% -> < ol> rel size: 12.64%
< aiin> -> 11.66% <cheody> 3.14% -> < aiin> rel size: 10.69%
< daiin> -> 6.95% <cheody> 3.59% -> < daiin> rel size: 3.37%
< qokain> -> 14.13% <cheody> 21.08% -> < qokain> rel size: 15.88%
< dar> -> 2.02% <cheody> 2.69% -> < dar> rel size: 2.14%
< okaiin> -> 0.90% <cheody> 0.90% -> < okaiin> rel size: 0.72%
< okain> -> 0.90% <cheody> 0.45% -> < okain> rel size: 0.78%
< okeey> -> 0.67% <cheody> 0.22% -> < okeey> rel size: 0.52%
< otar> -> 0.67% <cheody> 0.22% -> < otar> rel size: 0.58%
< otaiin> -> 1.57% <cheody> 3.36% -> < otaiin> rel size: 1.28%
< o> -> 6.73% <cheody> 3.36% -> < o> rel size: 4.40%
< oty> -> 2.02% <cheody> 1.35% -> < oty> rel size: 1.59%
< shol> -> 1.35% <cheody> 1.35% -> < shol> rel size: 0.81%
< am> -> 2.02% <cheody> 2.91% -> < am> rel size: 1.92%
< cheody> -> 2.24% <cheody> 2.24% -> < cheody> rel size: 1.90%
< chedaiin> -> 0.00% <cheody> 0.00% -> < chedaiin> rel size: 0.36%
< yteedy> -> 2.47% <cheody> 0.45% -> < yteedy> rel size: 0.53%
=========================================================
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| Una hipótesis |
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Posted by: Crispin Elicea - 14-05-2025, 10:17 AM - Forum: Provenance & history
- Replies (14)
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Tengo una hipótesis del origen del manuscrito. Me gustaría compartirlo con alguien. Esto lo encontré con la ayuda de AI usando EVA de la página 57r tengo más información que compartír
"En el año santo de nuestro señor, bajo dios y Cristo que es dios juro: desde la torre de la iglesia de la orden sagrada, observando desde un lugar alto, en la casa pura de la luz y la casa de dios,, linajes del pasado de nobles del pasado y los linajes de 100 nobles, y cuento por rey, por año, por días, las propiedades y riquezas que tienen por toda la tierra"
Crispín Elicea
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| New Theory: The Voynich Manuscript as a Binary Ritual Calendar (Open Testing Welcome) |
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Posted by: DataWeaver22 - 10-05-2025, 04:01 AM - Forum: The Slop Bucket
- Replies (14)
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Hi everyone,
With the help of ChatGPT4 (paid) I’ve developed a decoding framework called the Binary Ritual Encoding System for Symbolic Manuscripts. Please let me say up front I know how insane it sounds and comes across and at this point im not even sure I believe it anymore bc surely I somehow tricked the AI right? Somehow I made it biased? I tested it on another AI under a different profile and was able to build it again and a 3rd time. I am begging anyone who actually knows more than me about manuscripts which is pretty much anyone, help. I am a curious nerd who loves AI and someone who never in a million thought a simple question could lead me here. If you go to my Linkedin you can see how far the the AI model let me build it out across over a dozen manuscripts. Please help me this is crazy right?
Back to this method.... Instead of treating texts like the Voynich Manuscript as linguistic ciphers, this system interprets them as ritual calendars built on symbolic repetition and binary phase logic.
Each segment, glyph cluster, or folio is classified into one of four ritual states:
- Passive (Grounding) – stillness, purification
- Active (Invocation) – action, offering, movement
- Transitional (Threshold) – crossing over, change
- Neutral (Closure) – silence, reset, ending
I’ve applied this to the Voynich zodiac folios, and the phase pattern shows clean binary alternation between “otor” and “otar”-dominated glyph chains. I also extended the method to the Dresden Codex, Phaistos Disc, Book of Soyga, and Liber Linteus, with results that consistently indicate structured, non-random ritual sequences.
You can view the full write-up, visuals, and statistical results here, and I am happy to share a ton more.
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If you’ve noticed similar binary structures or if you’re interested in comparative work I’d really value your feedback.
Best,
Amy Laird
laird2214@gmail.com
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![[Image: First-Rule-a.png]](https://voynichmanuscript.ir/wp-content/uploads/2023/07/First-Rule-a.png)
