What I took away from Rene's findings is his conclusion that "the information contained in Voynich MS words is similar to that in Latin or Italian words, but the information is more equally distributed over the characters while the words are shorter."
A first thing to note is that he used the Cuva alphabet, which I agree is a better standard than EVA for statistical analysis. Cuva reduces the predictability introduced by EVA, which has the effect that words in Cuva are shorter. When using EVA, I would predict a less uniform entropy distribution, because things like EVA-n will have an effect on entropy statistics in a very specific spot.
But when these things are remedied (as with Cuva), my understanding of what Rene wrote is that the entropy distribution within a VM word is more even than in Latin. I am not sure if I can understand this intuitively though. Is it because the "options" in Voynichese are lower for each given spot, that the next spot will still have a relatively large impact on the information content?
Why does the entropy per character decrease in Latin from the first to the second and so on? Do I understand it correctly that the first character could be any character (low predictability), but then the second character must be able to combine with the first, so its predictability is higher?
Anyway, to answer your question, if I understood everything correctly, I would predict that reversing the direction would lead to similar results. There is not a single hotspot of information content in a VM word, but the combination of parts still makes it so that word entropy is pretty normal.
A first thing to note is that he used the Cuva alphabet, which I agree is a better standard than EVA for statistical analysis. Cuva reduces the predictability introduced by EVA, which has the effect that words in Cuva are shorter. When using EVA, I would predict a less uniform entropy distribution, because things like EVA-n will have an effect on entropy statistics in a very specific spot.
But when these things are remedied (as with Cuva), my understanding of what Rene wrote is that the entropy distribution within a VM word is more even than in Latin. I am not sure if I can understand this intuitively though. Is it because the "options" in Voynichese are lower for each given spot, that the next spot will still have a relatively large impact on the information content?
Why does the entropy per character decrease in Latin from the first to the second and so on? Do I understand it correctly that the first character could be any character (low predictability), but then the second character must be able to combine with the first, so its predictability is higher?
Anyway, to answer your question, if I understood everything correctly, I would predict that reversing the direction would lead to similar results. There is not a single hotspot of information content in a VM word, but the combination of parts still makes it so that word entropy is pretty normal.