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3. Randomization

3-2. Primer: Markov chains (cont.). . 3-3. Discrete time Markov chains. time is discrete, t = 1, 2, system occupies state X(t) at time tX(t) takes values from finite state space Stransitions between states Pi,j = P(X(t 1) = j | X(t) = i ) probability system transits to j from iP = [Pi,j]

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3. Randomization

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