Discrete-time stochastic processes

Thus the restriction on the transition probabilities

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Unformatted text preview: Fij (1) ≤ xij for each i. We have defined a state j to be recurrent if Fj j (1) = 1 and have seen that if j is recurrent, then the returns to state j , given X0 = j form a renewal process, and all of the results of renewal theory can then be applied to the random sequence of integer times at which j is entered. Our next ob jective is to show that all states in the same class as a recurrent state are also recurrent. Recall that two states are in the same class if they communicate, i.e., each has a path to the other. For finite-state Markov chains, the fact that either all states in the same class are recurrent or all transient was relatively obvious, but for countable-state Markov chains, the definition of recurrence has been changed and the above fact is no longer obvious. We start with a lemma that summarizes some familiar results from Chapter 3. Lemma 5.1. Let {Nj j (t); t ≥ 0} be the counting process for occurrences of state j up to time t in a Markov chain with X0 = j . The fol lowing conditions are then equivalent. 1. state j is recurrent. 2. limt→1 Nj j (t) = 1 with probability 1. 3. limt→1 E [Nj j (t)] = 1. 4. limt→1 P n 1≤n≤t Pj j = 1. 5.1. INTRODUCTION AND CLASSIFICATION OF STATES 201 Proof: First assume that j is recurrent, i.e., that Fj j (1) = 1. This implies that the inter-renewal times between occurrences of j are IID rv’s, and consequently {Nj j (t); t ≥ 1} is a renewal counting process. Recall from Lemma 3.1 of Chapter 3 that, whether or not the expected inter-renewal time E [Tj j ] is finite, limt→1 Nj j (t) = 1 with probability 1 and limt→1 E [Nj j (t)] = 1. Next assume that state j is transient. In this case, the inter-renewal time Tj j is not a rv, so {Nj j (t); t ≥ 0} is not a renewal process. An eventual return to state j occurs only with probability Fj j (1) < 1, and, since subsequent returns are independent, the total number of returns to state j is a geometric rv with mean Fj j (1)/[1 − Fj j (1)]. Thus the total number of returns is finite with probability 1 and the expected total number of returns is finite. This establishes the first three equivalences. Finally, note that Pjn , the probability of a transition to state j at integer time n, is equal j to the expectation of a transition to j at integer time n (i.e., a single transition occurs with probability Pjn and 0 occurs otherwise). Since Nj j (t) is the sum of the number of j transitions to j over times 1 to t, we have X E [Nj j (t)] = Pjn , j 1≤n≤t which establishes the final equivalence. Next we show that if one state in a class is recurrent, then the others are also. Lemma 5.2. If state j is recurrent and states i and j are in the same class, i.e., i and j communicate, then state i is also recurrent. P Proof: From Lemma 5.1, state j satisfies limt→1 1≤n≤t Pjn = 1. Since j and i commuj m nicate, there are integers m and k such that Pij > 0 and Pjki > 0. For every walk from state j to j in n steps, there is a corresponding walk from i to i in m + n + k steps, going from i to j in m steps, j to j in n steps, and j back to i in k steps. Thus m m Pii +n+k ≥ Pij Pjn Pjki j 1 X n=1 n Pii ≥ 1 X n=1 m m Pii +n+k ≥ Pij Pjki 1 X n=1 Pjn = 1. j Thus, from Lemma 5.1, i is recurrent, completing the proof. Since each state in a Markov chain is either recurrent or transient, and since, if one state in a class is recurrent, all states in that class are recurrent, we see that if one state in a class is transient, they all are. Thus we can refer to each class as being recurrent or transient. This result shows that Theorem 4.1 also applies to countable-state Markov chains. We state this theorem separately here to be specific. Theorem 5.1. For a countable-state Markov chain, either al l states in a class are transient or al l are recurrent. 202 CHAPTER 5. COUNTABLE-STATE MARKOV CHAINS We next look at the delayed counting process {Nij (n); n ≥ 1}. We saw that for finitestate ergodic Markov chains, the effect of the starting state eventually dies out. In order to find the conditions under which this happens with countable-state Markov chains, we compare the counting processes {Nj j (n); n ≥ 1} and {Nij (n); n ≥ 1}. These differ only in the starting state, with X0 = j or X0 = i. In order to use renewal theory to study these counting processes, we must first verify that the first-passage-time from i to j is a rv. The following lemma establishes the rather intuitive result that if state j is recurrent, then from any state i accessible from j , there must be an eventual return to j . Lemma 5.3. Let states i and j be in the same recurrent class. Then Fij (1) = 1. Proof: First assume that Pj i > 0, and assume for the sake of establishing a contradiction that Fij (1) < 1. Since Fj j (1) = 1, we can apply (5.5) to Fj j (1), getting X X 1 = Fj j (1) = Pj j + Pj k Fkj (1) < Pj j + Pj k = 1, k6=j k6=j where the strict inequality follows since Pj i Fij (1) < Pj i by assumption. This is a contradiction, so Fij (1) = 1 for every i accessible from j in one step. Next assume that Pj2i > 0, say wit...
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This note was uploaded on 09/27/2010 for the course EE 229 taught by Professor R.srikant during the Spring '09 term at University of Illinois, Urbana Champaign.

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