Markov reads Puškin, again: A statistical journey into the poetic world of Evgenij Onegin

04 Jun 20264 min read0 viewsJournal Feed

GIST (Key Takeaways)

by Angelo Maria Sabatini This study applies symbolic time series analysis and Markov modeling to explore the phonological structure of Evgenij Onegin —as captured through a graphemic vowel/consonant (V/C) encoding—and one contemporary Italian translation. Using a binary encoding inspired by Markov’s original scheme, we construct minimalist probabilistic models that capture both local V/C dependencies and large–scale sequential patterns.
A compact four-state Markov chain is shown to be descriptively accurate and generative, reproducing key features of the original sequences such as autocorrelation and memory depth. All findings are exploratory in nature and aim to highlight structural regularities while suggesting hypotheses about underlying narrative dynamics.
The analysis reveals a marked asymmetry between the Russian and Italian texts: the original exhibits a gradual decline in memory depth, whereas the translation maintains a more uniform profile. To further investigate this divergence, we introduce phonological probes — short symbolic patterns that link surface structure to narrative-relevant cues.
Tracked across the unfolding text, these probes reveal subtle connections between graphemic form and thematic development, particularly in the Russian original.