Discrete-time stochastic processes

# We 318 chapter 7 random walks large deviations and

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Unformatted text preview: n−1 + Yn−1 , we can express Zn as Zn = Zn−1 + Yn−1 − g + wXn − wXn−1 . Since E [Yn−1 | Xn−1 = i] = Y i and E [wXn | Xn−1 = i] = P j (7.56) Pij wj , we have E [Zn | Zn−1 , Zn−2 , . . . , Z1 , Xn−1 = i] = Zn−1 + Y i − g + X j Pij wj − wi . (7.57) From (7.53) the ﬁnal four terms in (7.57) sum to 0, so E [Zn | Zn−1 , . . . , Z1 , Xn−1 = i] = Zn−1 . (7.58) Since this is valid for all choices of Xn−1 , we have E [Zn | Zn−1 , . . . , Z1 ] = Zn−1 . Since the expected values of all the reward variables Y i exist, we see that E [|Yn |] &lt; 1, so that E [|Zn |] &lt; 1 also. This veriﬁes that {Zn ; n ≥ 1} is a martingale. It can be veriﬁed similarly that E [Z1 ] = 0, so E [Zn ] = 0 for all n ≥ 1. 7.6. MARTINGALES AND SUBMARTINGALES 303 In showing that {Zn ; n ≥ 1} is a martingale, we actually showed something a little stronger. That is, (7.58) is conditioned on Xn−1 as well as Zn−1 , . . . , Z1 . In the same way, it follows that for all n &gt; 1, E [Zn | Zn−1 , Xn−1 , Zn−2 , Xn−2 , . . . , Z1 , X1 ] = Zn−1 . (7.59) In terms of the gambling analogy, this says that {Zn ; n ≥ 1} is fair for each possible past sequence of states. A martingale {Zn ; n ≥ 1} with this property (i.e., satisfying (7.59)) is said to be a martingale relative to the joint process {Zn , Xn ; n ≥ 1}. We will use this martingale later to discuss threshold crossing problems for Markov modulated random walks. We shall see that the added property of being a martingale relative to {Zn , Xn } gives us added ﬂexibility in deﬁning stopping times. As an added bonus to this example, note that if {Xn ; n ≥ 0} is taken as the embedded chain of a Markov process (or semi-Markov process), and if Yn is taken as the time interval from transition n to n + 1, then Sn becomes the epoch of the nth transition in the process. 7.6.3 Generating functions for Markov random walks Consider the same Markov chain and reward variables as in the previous example, and assume that for each pair of states, i, j , the moment generating function gij (r) = E [exp(rYn ) | Xn = i, Xn+1 = j ] . (7.60) exists over some open interval (r− , r+ ) containing 0. Let [Γ(r)] be the matrix with terms Pij gij (r). Since [Γ(r)] is an irreducible non-negative matrix, Theorem 4.6 shows that [Γ(r)] has a largest real eigenvalue, ρ(r) &gt; 0, and an associated positive right eigenvector, ∫ (r) = (∫1 (r), . . . , ∫J (r))T that is unique within a scale factor. We now show that the process {Mn (r); n ≥ 1} deﬁned by Mn (r) = exp(rSn )∫Xn (r) . ρ(r)n ∫k (r) (7.61) is a product type Martingale for each r ∈ (r− , r+ ). Since Sn = Sn−1 + Yn−1 , we can express Mn (r) as Mn (r) = Mn−1 (r) exp(rYn−1 ) ∫Xn (r) . ρ(r)∫Xn−1 (r) The expected value of the ratio in (7.62), conditional on Xn−1 = i, is P ∑ ∏ exp(rYn−1 )∫Xn (r) j Pij gij (r )∫j (r ) E | Xn−1 =i = = 1. ρ(r)∫i (r) ρ(r)∫i (r) (7.62) (7.63) where, in the last step, we have used the fact that ∫ (r) is an eigenvector of [Γ(r)]. Thus, E [Mn (r) | Mn−1 (r), . . . , M1 (r), Xn−1 = i] = Mn−1 (r). Since this is true for all choices of i, the condition on Xn−1 = i can be removed and {Mn (r); n ≥ 1} is a martingale. Also, for n &gt; 1, E [Mn (r) | Mn−1 (r), Xn−1 , . . . , M1 (r), X1 ] = Mn−1 (r). (7.64) 304 CHAPTER 7. RANDOM WALKS, LARGE DEVIATIONS, AND MARTINGALES so that {Mn (r); n ≥ 1} is also a martingale relative to the joint process {Mn (r), Xn ; n ≥ 1}. It can be veriﬁed by the same argument as in (7.63) that E [M1 (r)] = 1. It then follows that E [Mn (r)] = 1 for all n ≥ 1. One of the uses of this martingale is to provide exponential upper bounds, similar to (7.16), to the probabilities of threshold crossings for Markov modulated random walks. Deﬁne exp(rSn ) minj (∫j (r)) f Mn (r) = . (7.65) ρ(r)n ∫k (r) h i f f Then Mn (r) ≤ Mn (r), so E Mn (r) ≤ 1. For any µ &gt; 0, the Markov inequality can be f applied to Mn (r) to get n o i 1 hf 1 f Pr Mn (r) ≥ µ ≤ E Mn (r) ≤ . µ µ (7.66) For any given α, and any given r, 0 ≤ r &lt; r+ , we can choose µ = exp(rα)ρ(r)−n minj (∫j (r))/∫k (r), and for r &gt; 0. Combining (7.65) and (7.66), Pr {Sn ≥ α} ≤ ρ(r)n exp(−rα)∫k (r)/ min(∫j (r)). j (7.67) This can be optimized over r to get the tightest bound in the same way as (7.16). 7.6.4 Scaled branching processes A ﬁnal example of a martingale is a “scaled down” version of a branching process {Xn ; n ≥ 0}. Recall from Section 5.2 that, for each n, Xn is deﬁned as the aggregate number of elements in generation n. Each element i of generation n, 1 ≤ i ≤ Xn has a number Yi,n P of oﬀspring which collectively constitute generation n + 1, i.e., Xn+1 = Xn Yi,n . The rv’s i=1 Yi,n are IID over both i and n. Let Y = E [Yi,n ] be the mean number of oﬀspring of each element of the population. Then E [Xn | Xn−1 ] = Y Xn−1 , which resembles a martingale except for the factor of Y . We can c...
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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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