Torsten > 04-11-2019, 11:38 AM
(04-11-2019, 10:14 AM)Koen G Wrote: You are not allowed to view links. Register or Login to view.
ReneZ > 04-11-2019, 01:45 PM
MarcoP > 04-11-2019, 03:21 PM
(02-11-2019, 08:26 PM)ReneZ Wrote: You are not allowed to view links. Register or Login to view.I am beginning to wonder if one of the main features of the Voynich MS text is that it is really page-oriented, i.e. properties are quite page-dependent. This would fit with an encyclopedic work (like Pliny).
nablator > 04-11-2019, 04:06 PM
(04-11-2019, 03:21 PM)MarcoP Wrote: You are not allowed to view links. Register or Login to view.(02-11-2019, 08:26 PM)ReneZ Wrote: You are not allowed to view links. Register or Login to view.I am beginning to wonder if one of the main features of the Voynich MS text is that it is really page-oriented, i.e. properties are quite page-dependent. This would fit with an encyclopedic work (like Pliny).My impression is that, while it is possible that specific subjects are partially responsible for how similar words cluster together, this explanation is not sufficient. Aren't the differences between sections too large for a word-to-word mapping of a uniform language?
Torsten > 04-11-2019, 08:16 PM
(04-11-2019, 04:06 PM)nablator Wrote: You are not allowed to view links. Register or Login to view.- are vords words? If not, there is no problem with using different (ciphertext) vords for encoding the same (plaintext) words, and a one-to-one vord-to-word mapping model is irrelevant,
nablator > 04-11-2019, 09:33 PM
(04-11-2019, 08:16 PM)Torsten Wrote: You are not allowed to view links. Register or Login to view.Keep in mind that the VMS token strings fulfill both of Zipf’s laws. This fact is frequently seen as important evidence for the presence of human language and that vords are words (see You are not allowed to view links. Register or Login to view.). A different mapping would also result in changed statistics. Therefore the mapping should preserve the word frequencies. Otherwise a different explanation for the observed word frequencies would be necessary.
Common_Man > 05-11-2019, 09:52 PM
Torsten > 06-11-2019, 10:52 PM
(29-10-2019, 09:17 AM)ReneZ Wrote: You are not allowed to view links. Register or Login to view.I didn't mean to say that the long papers are bad. Quite the contrary: they contain a lot of valuable statistics. It is just that it makes it more time-consuming to ingest it all.
(29-10-2019, 09:17 AM)ReneZ Wrote: You are not allowed to view links. Register or Login to view.From the very first mention of the idea I considered that this process is too arbitrary and does not explain some of the most important properties of the text. I still think so but it is qualitative and not sufficient to decide if it can be true or not.
(29-10-2019, 09:17 AM)ReneZ Wrote: You are not allowed to view links. Register or Login to view.The near-repetitions are explained, the appearance of Eva-f and p primarily (but not at all exclusively) in top lines of paragraphs is explained, but the word structure is not. And there are a few pother things, already mentioned before.
(29-10-2019, 09:17 AM)ReneZ Wrote: You are not allowed to view links. Register or Login to view.So I try to imagine how this would have arisen. This puts some constraints on how the system is initialised and how the changes are applied.
(29-10-2019, 09:17 AM)ReneZ Wrote: You are not allowed to view links. Register or Login to view.The discussion is quite similar to that in 2004 or so when Gordon Rugg presented his method. To take into account the known properties of the text, the method will require numerous dedicated adaptations, and here is where Occam's razor comes into play. Not that it is proof of anything. It is a sign.
Alin_J > 08-02-2020, 12:34 PM
MarcoP > 09-02-2020, 11:21 AM
(08-02-2020, 12:34 PM)Alin_J Wrote: You are not allowed to view links. Register or Login to view.Hello. I did some statistical analysis on the most frequent repeated word-sequences, and some analysis on the transition relationships between characters with principal component analysis (PCA) on the same auto-generated text that Timm & Schinner used in the article, compared to an excerpt of the Voynich manuscript (the 'recipes' section, f103r-116v, the same excerpt they were aiming to simulate with their algorithm in the article). I used my routines written in C++ to extract and assemble the information.
Comments on the results text files (attached)
The sizes of both texts, measured in number of words (tokens), is similar: 10832 vs 10681. However, Timm's and Schinner's algorithm under-predicts the number of unique words somewhat (2228 vs 3103 in the Voynich text). The frequency distribution of the most frequent words seems similar, except for the three most frequent words, which are over-predicted by approximately twice the amounts.