(29-12-2025, 05:38 AM)MHTamdgidi_(Behrooz) Wrote: You are not allowed to view links. Register or Login to view.But the problem is how to tell if a text is of one or another type if each word has multiple meanings, especially as found in poetry contexts when use of metaphors and double meanings are expected.
Let’s say we have a statement A B C D E, where each has a variety of meanings and connotations corresponding to it, as 1-5, from most apparent to more hidden. The thread A1-B3-C2-D5-E4 may sound incoherent and readily dismissable, but the thread A3-B1-C4-D2-E5 may be meaningful.
After a theory has enough substance it's possible to use simple mathematical methods to estimate how likely it is to create a reproducible (unique well defined) solution. There are many people on the forum who are familiar with Bayesian analysis (or whatever it's called) who probably can compute some scores in science friendly way. I can compute some simple naive probabilities, they are good enough for me.
For example, let's take
doary = botrus match. How likely is it to be accidental? The below is not an exact calculation, but more of a template. If we consider all the labels in the manuscript and assume Greek or Latin source:
1) I think there are at least 30 words in Latin (or Greek) that one would consider semantically relevant for this label (used to describe the Pleiades), words like: group, pile, seven, sister, gathering, star, proper names like Taurus, Orion, Pleiades, etc. So let's assume for an average label there are about 30 candidates that one would consider meaningful.
2) There is one letter mismatch, if we allow a one letter mistake and match all other letters reasonably by shape, with the alphabet size of 20 characters there will be 19*5 = 95 possible different readings for a five letter label. If we also use scribal abbreviations, this will expand the set of readings even further, let's say 400 combinations.
3) Now we need to get the total size of the corpus, suppose we limit ourselves to 20000 most common words. Given 400 sequences, how many of these would end up among valid words for the language? We can try using entropy bits per character or run a simulation on this, so this one need some further research, but I don't think it's unreasonable to assume that 400 letter sequences will on average produce 10 valid words (given that
o and
a look like vowels and appear in most words, this doesn't look very unreasonable, most sequences are quite word-like).
4) Given the corpus of 20000 words of which 30 words would be semantically relevant if we select N words at random, how likely are we to get one of the relevant words? To simplify a bit, we have the probability of 30/20000 or 0.0015 at each try. We perform 10 tries per label in the manuscript. There are at least 40 labels attached to images, where one can claim semantical relevance. So, we have 400 tries in total:
1 - (1 - 30/20000) ** 400 = ~0.45
So, this back of the envelope computation gives 45% probability of a single coincidental match as plausible as
doary = botrus somewhere in the manuscript.
It's most certain that this number can be challenged, but in general a spurious match for one label is certainly not impossible. However, if we add more and more labels matched according to the same principle, the probability of a chance match will drop dramatically. For example, if you demonstrated that your method produces 5 plausible matches for 5 different labels, for me personally this would move your result significance from "likely curious coincidence" to something that certainly requires a proper explanation. As it stands now
doary = botrus on its own doesn't require any explanation at all for me, pure coincidence works just fine.
For any proposed deciphering method, including your A3-B1-C4-D2-E5 example above, one can roughly compute the likelihood in a similar fashion.
(29-12-2025, 05:38 AM)MHTamdgidi_(Behrooz) Wrote: You are not allowed to view links. Register or Login to view.Regarding your previous reply, sorry that I missed the last line. Yes, I would be interested in seeing your set of labels which may come of use later.
Here they are:
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