Markov Chains
Markov Chains are mathematical models that describe a system that transitions between different states over time. In the context of data science, Markov Chains are used to model sequences of events or states, where the probability of transitioning from one state to another depends only on the current state and not on any previous states. Markov Chains are widely used in natural language processing, speech recognition, and image processing, among other fields. They are also used in finance to model stock prices and in biology to model the behavior of cells. Markov Chains can be represented as a matrix, where each row represents the probabilities of transitioning from one state to another. The steady-state distribution of a Markov Chain can be calculated to determine the long-term behavior of the system.
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