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What is Understanding Markov Processes in Depth about?

Markov processes model systems that have limited memory of their past. They are used widely in engineering, science, and business. Markov processes can be classified based on whether their state space and time parameters are discrete or continuous. Discrete-time Markov chains (DTMCs) are an important type of Markov process where the future state depends only on the present state, not on past states. DTMCs can be represented using state transition probability matrices where each entry gives the probability o

Author
Harsh Upadhayay
Language
EN