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MethodsInformation theory (Bentz et al.)Entropy law and word-order gain
Method · information theory (Bentz et al.)

Entropy law and word-order gain

Information theory (bentz et al.)Version 1.0Licence MIT4 runsUsed on 1 mystery

Questions it answers

Code Python

Download module (.py)t_entropy_law.pyView source

Runs and results 4 runs

RunMysteryMeasuredResultAgainstDate
RUN-028VoynichBits per word saved by word order (random word ids)0.418102 languages with word spaces: 1.34-4.10, median 2.3922 Oct
RUN-027VoynichResidual from the H1 vs entropy-rate law (word level), sd unitsh2: 1.95; compression: 3.65103 languages, same length22 Oct
RUN-023VoynichResidual from the H1 vs entropy-rate law (word level), sd unitsh2: 1.56; compression: 2.81103 languages, same length22 Oct
RUN-017VoynichResidual from the H1 vs entropy-rate law (word level), sd unitsh2: 1.8; compression: 2.86103 languages, same length22 Oct

Applied to

Voynich Manuscript
2 datasetsGC2a transcription, ZL3b transcription
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