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MIT6_262S11_lec12 - 6.262 Discrete Stochastic Processes...

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Theorem: If { Z n ; n 1 } converges to α WP1, (i.e., Pr { ω : lim n ( Z n ( ω ) α ) = 0 } = 1 ), and f ( x ) is continu­ ous at α . Then Pr { ω : lim n f ( Z n ( ω )) = α } = 1 . For a renew al process with inter-renew als X i , 0 < X < , Pr ω : lim n ( 1 S n ( ω ) X ) = 0 = 1 ) n n 1 Pr ω : lim = = 1 . n →∞ S n ( ω ) X For renewal processes, n/S n and N ( t ) /t are related by Slope = N ( t ) Slope = N ( t ) t S N ( t ) Slope = N ( t ) S N ( t )+1 N ( t ) t 0 S 1 S N ( t ) S N ( t )+1 2 6.262: Discrete Stochastic Processes 3/14/11 Lecture 12 : Renewal rewards, stopping trials, and Wald’s equality Outline: Review strong law for renewals Review of residual life Time-averages for renewal rewards Stopping trials for stochastic processes Wald’s equality Stop when you’re ahead 1
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The strong law for renewal processes follows from this relation between n/S n and N ( t ) /t . Theorem: For a renewal process with X < , Pr ω : lim N ( t, ω ) /t = 1 /X = 1 . t →∞ This says that the rate of renewals over the infinite time horizon (i.e., lim t N ( t ) /t ) is 1 /X WP1. This also implies the weak law for renewals, N ( t ) 1 lim Pr t →∞ t X > = 0 for all > 0 3 X 5 ( ω ) X 2 ( ω ) Y ( t, ω ) X 4 ( ω ) X X ( ω ) 6 ( ω 1 ❅❅ t S 1 ( ω ) S 2 ( ω ) S 4 ( ω ) S 5 ( ω ) S 6 ( ω ) N ( t, ω ) 2 N ( t, ω )+1 X ( ω ) 1 t 2 X ( ω ) i Y ( t, ω ) dt i 2 t t 0 n 2 t =1 n =1 Review of residual life Def: The residual life Y ( t ) of a renewal process at time t is the remaining time until the next renewal, i.e., Y ( t ) = S N ( t )+1 t . Residual life is a random process; for each sample point ω , Y ( t, ω ) is a sample function. 4
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N ( t, ω ) ( )+1 2 N t, ω 2 X ( ω ) 1 X i t ( ω ) Y ( t, ω ) dt 2 t 0 i t 2 t i =1 i =1 Going to the limit t → ∞ ( 1 N t, ω ) t 2 X ( ω ) N ( t, ω ) lim Y ( t, ω ) dt = lim i t →∞ t 0 t →∞ 2 N ( t, ω ) t n =1 E 2 X = 2 E [ X ] This is infinite if E 2 X = . Think of example where p X ( ) = 1 , p X (1 / ) = .
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