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In 1906, Andrey Markov challenged classical probability by introducing processes where each step remembers only the present — not the past. These Markov chains proved that even with dependence, long-term averages converge to stable patterns. Today, this idea powers Monte Carlo simulations, AI learning systems, hidden Markov models, physics, biology, and modern data science. Cinematic visuals and audio generated with AI. Source: Andrey Markov (1906) / Stanford Encyclopedia of Philosophy / MIT Pro
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