Markov Process
Markov Process is a stochastic process that satisfies 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 its current state. Markov processes are widely used in various fields, including physics, chemistry, economics, and computer science. In data science and artificial intelligence, Markov processes are used in natural language processing, speech recognition, image processing, and reinforcement learning.
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