First Links in the Markov Chain
Probability and poetry were unlikely partners in the creation of a computational tool
One hundred years ago the Russian mathematician A. A. Markov founded a new branch of probability theory by applying mathematics to poetry. Delving into the text of Alexander Pushkin’s novel in verse Eugene Onegin, Markov spent hours sifting through patterns of vowels and consonants. On January 23, 1913, he summarized his findings in an address to the Imperial Academy of Sciences in St. Petersburg. His analysis did not alter the understanding or appreciation of Pushkin’s poem, but the technique he developed—now known as a Markov chain—extended the theory of probability in a new direction. Markov’s methodology went beyond coin-flipping and dice-rolling situations (where each event is independent of all others) to chains of linked events (where what happens next depends on the current state of the system).
Markov chains are everywhere in the sciences today. Methods not too different from those Markov used in his study of Pushkin help identify genes in DNA and power algorithms for voice recognition and web search. In physics the Markov chain simulates the collective behavior of systems made up of many interacting particles, such as the electrons in a solid. In statistics, the chains provide methods of drawing a representative sample from a large set of possibilities. And Markov chains themselves have become a lively area of inquiry in recent decades, with efforts to understand why some of them work so efficiently—and some don’t.
As Markov chains have become commonplace tools, the story of their origin has largely faded from memory. The story is worth retelling. It features an unusual conjunction of mathematics and literature, as well as a bit of politics and even theology. For added drama there’s a bitter feud between two forceful personalities. And the story unfolds amid the tumultuous events that transformed Russian society in the early years of the 20th century.
Before delving into the early history of Markov chains, however, it’s helpful to have a clearer idea of what the chains are and how they work.
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