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Markov chains are mathematical systems that model sequences of events where the probability of each event depends only on the state attained in the previous event, making them ideal for predicting random processes over time. In modern applications, they're crucial for fields like machine learning and AI, where they help in areas such as speech recognition and financial forecasting, but they can oversimplify real-world complexities if not calibrated properly.
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