Data Science Numerical Analysis
Markov chains are mathematical systems that undergo transitions from one state to another within a finite or countable number of possible states. They are characterized by the Markov property, which states that the future state of a process depends only on its current state and not on the sequence of events that preceded it. This property makes Markov chains particularly useful in modeling a variety of stochastic processes, including those encountered in optimization algorithms.
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