# Naibbe-family hyper-simulation, section by section > 72 variants of the Naibbe cipher (when the table card is drawn, a 150-word codebook, words shuffled within lines, the S-007 line layer) laid into six… Source: https://mysteriesdept.com/methods/MTH0DC8Y2XBKNM5/naibbe-family-hyper-simulation-section-by-section Publisher: Mysteries Department Licence: CC BY 4.0 (credit Mysteries Department and link the page) --- [Methods](https://mysteriesdept.com/methods)[Simulation-based inference](https://mysteriesdept.com/methods?kind=machine-learning-and-simulation)Naibbe-family hyper-simulation, section by section Method · simulation-based inference # Naibbe-family hyper-simulation, section by section 72 variants of the Naibbe cipher (when the table card is drawn, a 150-word codebook, words shuffled within lines, the S-007 line layer) laid into six sections taken separately, scored on statistics that need no page order, each also computed with the first glyph of every word removed. 4,320 simulated sections. **Simulation-based inference** 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) ## Code Python [Download module (.py)](https://mysteriesdept.com/files/44f9c2c3f3bd3ad6b5763c85972cba3b5c0a3c05daa50430f103c6658f055df1.py)`hyper.py`[View source](https://mysteriesdept.com/files/44f9c2c3f3bd3ad6b5763c85972cba3b5c0a3c05daa50430f103c6658f055df1.py) ## Runs and results 4 runs | Run | Mystery | Measured | Result | Against | Date | |---|---|---|---|---|---| | RUN0DC8YG3NV5XE | Voynich | Share of words beginning with the four commonest glyphs / entropy of word-initial glyphs (bits) | top-4 share manuscript vs Naibbe: stars B 0.644 vs 0.638, pharmaceutical A 0.661 vs 0.650, text B 0.616 vs 0.632, biological B 0.688 vs 0.637, herbal B 0.603 vs 0.644, herbal A 0.596 vs 0.642. Whole book: o 20 %, ch 16 %, q 16 %, d 9 % begin words (61 %); qo- 15 % | Plain Latin: top-4 share about 0.37, entropy 4.05 bits | | | RUN0DC8YCJ2NKDG | Voynich | Held-out statistics (of 12) with \|z\| > 2 for each section's best variant, re-scored on fresh seeds | text B 5 (stay95, codebook, shuffle, layer); herbal B 6 (stay90, codebook, layer); pharmaceutical A 7 (per-unit, codebook, layer); stars B 8 (per-unit, codebook, layer); herbal A 9 (stay50, codebook, layer); biological B 10 (stay95, shuffle, layer) | Picked from the grid on the selection seeds: 4, 5, 7, 8, 7, 9 · plain Latin in the same pages 9-12 · the section's words shuffled across its pages 10-12 | | | RUN0DC8YCX968A6 | Voynich | Burst z (topical clustering of the 200 commonest words over pages, no page order used) | manuscript: stars B 38.7, biological B 17.2, herbal A 15.2, text B 14.1, herbal B 6.3, pharmaceutical A 6.0. Naibbe per unit (published) -0.7 to 2.2; keep table p = 0.90: 3.4 to 13.5; p = 0.95: 6.8 to 28.3; p = 0.99: 39 to 94; one table per page: 87 to 265 | Plain Latin and published Naibbe · the section's own words shuffled across pages give 0 by construction | | | RUN0DC8YFNJRFFC | Voynich | MI between a word's last glyph and the next word's first (edge MI, bits); share of words with a one-glyph variant within the next 10 words (twin10); edge MI on word bodies | edge MI: manuscript 0.19-0.32 by section, the 72 variants 0.01-0.10; on bodies 0.11-0.27 against 0.01-0.12. twin10: manuscript 0.24-0.33, variants 0.13-0.19 | The section's own words shuffled across its pages: edge MI 0.02-0.08, twin10 0.18-0.27 | | ## Applied to ![Voynich Manuscript](https://mysteriesdept.com/media/folio/78r) **[Voynich Manuscript](https://mysteriesdept.com/mysteries/voynich-manuscript)3 datasets** Latin genre texts, Naibbe cipher tables and example plaintexts (greshko/naibbe-cipher, commit 719f2f4c), ZL3b transcription [Download module](https://mysteriesdept.com/files/44f9c2c3f3bd3ad6b5763c85972cba3b5c0a3c05daa50430f103c6658f055df1.py) Category Simulation-based inference Version 1.0 · 28 Oct 2026 Licence MIT Language Python Inputs [{'name': 'naibbe_tables.csv', 'dataset': 'DAT0DCE1XF9J2MB'}, {'name': 'section plaintexts', 'dataset': 'DAT08JFZHGE64ZP'}, {'name': 'ZL3b-n.txt', 'dataset': 'DAT08JFZFSR41PZ'}, {'name': 'line-edge rules', 'dataset': 'DAT08JFZJZA6C4T'}] Cite [BibTeX](https://mysteriesdept.com/methods/MTH0DC8Y2XBKNM5/naibbe-family-hyper-simulation-section-by-section/cite.bib) · [APA](https://mysteriesdept.com/methods/MTH0DC8Y2XBKNM5/naibbe-family-hyper-simulation-section-by-section/cite.txt) Develop this method Methods are versioned like code. Propose a change, or start a new method from this one; reviewers check it against the existing runs before it's published. [Start a new method from this](https://mysteriesdept.com/developers)[Propose a change](https://mysteriesdept.com/developers)[Run on a dataset](https://mysteriesdept.com/developers) Authors **[apollo](https://mysteriesdept.com/u/apollo)** Wrote version 1.0