Victoria Stuart πŸ‡¨πŸ‡¦ πŸ³οΈβ€βš§οΈAug 20, 2024

Analysis of Finnegans Wake for novel spacing between punctuation marks
https://phys.org/news/2024-08-finnegans-spacing-punctuation.html

Finnegans Wake: https://en.wikipedia.org/wiki/Finnegans_Wake
* Irish writer James Joyce
* allusive/experimental style
* one of most difficult works in literature: https://www.britannica.com/topic/Finnegans-Wake

Read: https://archive.org/details/in.ernet.dli.2015.207614/mode/2up
PDF: https://www.mobileread.com/forums/attachment.php?attachmentid=112940&d=1381141545
Excerpts: https://old.reddit.com/r/ProsePorn/comments/q81beh/two_excerpt_from_finnegans_wake

Weibull distribution: https://en.wikipedia.org/wiki/Weibull_distribution

#literature #JamesJoyce #FinnegansWake statistics #ProbabilityDistributions #WeibullDistribution

New research analyzes 'Finnegans Wake' for novel spacing between punctuation marks

Statistical analysis of classic literature has shown that the way punctuation breaks up text obeys certain universal mathematical relationships. James Joyce's tome "Finnegans Wake," however, famously breaks the rules of normal prose through its unusual, dreamlike stream of consciousness. New work in chaos theory, published in the journal Chaos, takes a closer look at how Joyce's challenging novel stands out, mathematically.

Fabrizio MusacchioAug 13, 2023

The #Wasserstein distance πŸ“, aka Earth Mover’s Distance (#EMD), provides a robust and insightful approach for comparing #ProbabilityDistributions πŸ“Š. I’ve composed a #Python tutorial 🐍 that explains the #OptimalTransport problem required to calculate EMD. It also shows how to solve the OT problem and calculate the EMD using the Python Optimal Transport (POT) library. Feel free to use and share it πŸ€—

🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/

Wasserstein distance and optimal transport

The Wasserstein distance, also known as the Earth Mover’s Distance (EMD), provides a robust and insightful approach for comparing probability distributions and finds application in various fields such as machine learning, data science, image processing, and information theory. In this post, we take a look at the optimal transport problem, required to calculate the Wasserstein distance, and how to calculate the distance metric in Python.

Fabrizio Musacchio