How multi-character substitution might explain the voynich’s strange entropy

[obelus]

I was actually working with perplexity

OK, and I was commenting on plots labelled "Entropy," in the interest of not multiplying jargon without necessity.  But the formal relationship between the two is clear enough.  Meanwhile I have had time to illustrate my comment with a graph.

Imagine a tireless Cuva Character Selector that generates symbols, independently, according to probabilities observed in a VMS transliteration.  By construction, the CCS's true entropy is 3.85 bits at all orders, shown as a blue dashed line below:
   
The blue dots represent calculated conditional entropy estimates if we have only 144 000 characters of output to analyze.  The dramatic decrease is a pure artifact of limited sample length.  Orders 1, 2, and 3 are reliable, n>3 falsely suggest correlations in the Character Selector.  The computational procedure is what it is, but the high-n results should not be interpreted as "entropy" in the received sense.  Maybe renaming it will help!

The unscrambled Cuva sample (continuous text) is shown as grey stars, in satisfactory agreement with your results.  They show that the VMS generator is correlated on a scale of 2-4 characters, before the supposed-entropy curve is degraded by its underlying numerical limitations.  Any features appearing farther to the right are suspect.  We already know that sample length shapes such curves;  to make a strong case that encoding method also has a differential effect on them, some kind of sample-length validations would help.  How large is your Docta Ignorantia?  and how is its length affected by the transformations applied?