# Entropy law and word-order gain

> Run 4 times on the site, with every result.

Source: https://mysteriesdept.com/methods/MTH08JFX9ZC4HW8/entropy-law-and-word-order-gain  
Publisher: Mysteries Department  
Licence: CC BY 4.0 (credit Mysteries Department and link the page)  

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[Methods](https://mysteriesdept.com/methods)[Information theory (Bentz et al.)](https://mysteriesdept.com/methods?kind=statistics-and-information)Entropy law and word-order gain

Method · information theory (Bentz et al.)

# Entropy law and word-order gain

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

## Questions it answers

- [Is it a cipher of a known language?](https://mysteriesdept.com/mysteries/voynich-manuscript/questions/QST08JG2PB04SQP/is-it-a-cipher-of-a-known-language)
- [Is it a natural language?](https://mysteriesdept.com/mysteries/voynich-manuscript/questions/QST08JG27P80HMJ/is-it-a-natural-language)

## Code Python

[Download module (.py)](https://mysteriesdept.com/files/4241270dbf710925fb8cf705bdb4e34914c1e9e365080ab014551513dc0280c3.py)`t_entropy_law.py`[View source](https://mysteriesdept.com/files/4241270dbf710925fb8cf705bdb4e34914c1e9e365080ab014551513dc0280c3.py)

## Runs and results 4 runs

| Run | Mystery | Measured | Result | Against | Date |
|---|---|---|---|---|---|
| RUN-028 | Voynich | Bits per word saved by word order (random word ids) | 0.418 | 102 languages with word spaces: 1.34-4.10, median 2.39 | 22 Oct |
| RUN-027 | Voynich | Residual from the H1 vs entropy-rate law (word level), sd units | h2: 1.95; compression: 3.65 | 103 languages, same length | 22 Oct |
| RUN-023 | Voynich | Residual from the H1 vs entropy-rate law (word level), sd units | h2: 1.56; compression: 2.81 | 103 languages, same length | 22 Oct |
| RUN-017 | Voynich | Residual from the H1 vs entropy-rate law (word level), sd units | h2: 1.8; compression: 2.86 | 103 languages, same length | 22 Oct |

## Applied to

![Voynich Manuscript](https://mysteriesdept.com/media/folio/78r)

**[Voynich Manuscript](https://mysteriesdept.com/mysteries/voynich-manuscript)2 datasets** GC2a transcription, ZL3b transcription

[Download module](https://mysteriesdept.com/files/4241270dbf710925fb8cf705bdb4e34914c1e9e365080ab014551513dc0280c3.py)

Category

Information theory (bentz et al.)

Version

1.0 · 25 Oct 2026

Licence

MIT

Language

Python

Cite

[BibTeX](https://mysteriesdept.com/methods/MTH08JFX9ZC4HW8/entropy-law-and-word-order-gain/cite.bib) · [APA](https://mysteriesdept.com/methods/MTH08JFX9ZC4HW8/entropy-law-and-word-order-gain/cite.txt)

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Authors

**[apollo](https://mysteriesdept.com/u/apollo)** Wrote version 1.0