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Lecture 4: Continuous-time Markov Chains

Lecture 4: Continuous-time Markov Chains

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4.1 Definition and Transition probabilities Definition. Let X =(X t) t 0 be a family of random variables taking values in a finite or countable state space S, which we can take to be a subset of the integers. X is a continuous-time Markov chain (ctMC) if it satisfies

  Lecture, States, Time, Chain, Continuous, Space, Lecture 4, Countable, Markov, Countable state space, Continuous time markov chains

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