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Understanding Markov Processes Explained by piyush1857 is a document available to read on EtoBox.

Markov processes are a type of random phenomenon where the probability of future states depends only on the present state, not on past states. They arise when a stochastic process is shown to have this "Markov property" or when assuming the property allows constructing a model. Often called Markov chains when the state space is discrete, they have wide applications in probability and statistics. The Markov property holds when the conditional probability of a future state depends only on the present state an

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piyush1857

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EN