Markov Processes

Markov Processes are stochastic processes that satisfy the Markov property, which states that the future state of the process depends only on the present state and not on the past states. In other words, the probability distribution of the future state of the process depends only on the current state and not on how the process arrived at that state. Markov Processes are widely used in Data Science and Artificial Intelligence for modeling various real-world phenomena, such as stock prices, weather patterns, and customer behavior. They are also used in natural language processing for tasks such as language generation and machine translation.
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