4 5 convergence theorem just as in discrete time we

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4 5 Convergence Theorem Just as in discrete time, we have a convergence theorem for continuous time Markov chains. In discrete time, we needed the assumptions that the chain was irreducible, aperiodic, and positive recurrent. In continuous time, it is impossible for the Markov chain to have a period; and thus we can drop the requirement that the chain must be aperiodic. Positive recurrence is equivalent to the existence of an invariant distribution; and irreducibility is equivalent to uniqueness of the invariant distribution. This is exactly the same as the discrete time theory. The statement of the convergence theorem is: if a continuous time Markov chain is irreducible and positive recurrent, then a unique invariant distribution β exists, and furthermore, for all states j , we have regardless of the initial state i : lim t →∞ P ( X t = j | X 0 = i ) = β j . (As stated above, we can replace the assumption of positive recurrence by the assumption that an invariant distribution exists.) Again, note that this is not a statement about the jump chain. For the jump chain, we would apply the convergence theorem from our discrete time theory. 6 Law of Large Numbers As in discrete time, there is a law of large numbers for continuous time Markov chains. We want to state a law of large numbers for average reward. Let f : X → R be a reward function. Then the law of large numbers for continuous time Markov chains says that long-run average reward converges to be expected reward against the invariant distribution. Formally, if a continuous time Markov chain is irreducible with invariant distribution β , then for any bounded reward function f , we have: lim t →∞ T 0 f ( X s ) ds T = i β i f ( i ) . As in discrete time, there is an important connection between the law of large numbers and first return times. Given a continuous time Markov chain { X t } , we let J 1 , J 2 , J 3 , . . . denote the times at which the chain makes a jump from one state to another. As in discrete time, we formally define the first return time for a state i . In discrete time, remember, the first return time was the first time that we returned to i , if we started in a state j = i ; and it was the second time that we returned to i , if we started in i . In continuous time, we make the same definition, but keeping track of visits to states. Thus, we formally define the first return time T i 1 as follows: T i 1 = first time after J 1 that the chain returns to i, if X 0 = i ; first time after 0 that the chain returns to i, if X 0 = i. The expected return time for state i is E [ T i 1 | X 0 = i ] . 5
We define the reward until the first return time to i as Y i 1 : Y i 1 = T i 1 0 f ( X s ) ds. Note that this matches the definition of the reward until the first return time to i that we made in discrete time. As in discrete time, the law of large numbers for continuous time Markov chains is proven using first return times. In particular, it is also true that: lim t →∞ T 0 f ( X s ) ds T = E [ Y i 1 | X 0 = i ] E [ T i 1 | X 0 = i ] That is, the long-run average reward is the same as the ratio of the expected reward we earn in one excursion from i to i

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