Finding patterns in Voynich words via a Hidden Markov Model

[quimqu]

Hello Mauro,

I try to explain:

By 'top', do you mean 'most frequent'?

Yes, in every state, there is a quantity of prefixes, stems and sufixes. The shown are the "top", which are the, let's say, sub-morphemes (prefixes, stems, or suffixes) that are most likely to appear when the HMM is in that state (but there are more, only the top are shown).

I'm not sure how to read the state graph. Ie., if I start at S3, which strings does it actually contain? All the six strings [o che she d o daiin] ('o' being duplicated because it's been classified both as a prefix and a stem)?

I suggest you click on the link to the html and see how the morphemes are distributed per "word". You can start in S3 with a part of the words (which may contain 3 subpart, let's sy sub-prefix, sub-stem and/or sub-sufix) then the most likely way to go is to S7 (as the arrow is thicker). In the html you can see word per word how the HMM understands it is constructed.

How can the outputs of S3 add up to 1.06? They should be < 1.

I see the arrow labels are not correct. S3->S7 is labelled as S1->S6... I think it is confusing. I'll try to fix it and update the post.