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Unformatted text preview: ECEN 646 Homework 6 Solutions 1 C ¸inlar Exercise (8.4), Chapter 4 We are interested in every 13 th arrival in a Poisson process. (a) The interarrival times in a Poisson process are exponentially dis tributed. The lucky arrivals are separated by 12 unlucky ones and so the time between 2 lucky arrivals is the sum of 13 exponential random variables i.e. an Erlang(13) random variable. f T ( t ) = λe λt ( λt ) 12 12! t ≥ (b) Let N t be the number of arrivals in the interval [0 ,t ]. The number of gifts M t given would then be equal to the number of integer multiples of 13 contained in N t . The event { M t = k } is the same as the event { 13 k ≤ N t ≤ 13 k + 12 } P ( M t = k ) = P (13 k ≤ N t ≤ 13 k + 12) = 12 summationdisplay i =0 P ( N t = 13 k + i ) = 12 summationdisplay i =0 e λt ( λt ) 13 k + i (13 k + i )! No further simplification is possible. 2 C ¸inlar Exercise (8.10), Chapter 4 We have a family of Poisson processes out of which we choose one based on the outcome of a random experiment. The outcome of the random ex periment takes value i with probability π ( i ). (a) Conditioned on the event { X = i } , the process N is a Poisson process with rate λ ( i ). So, the unconditional distribution of N t can be found by conditioning on the value of X . 1 P { N t = k } = summationdisplay i ∈ E P ( N t = k  X = i ) P ( X = i ) = summationdisplay i ∈ E π ( i ) e λ ( i ) t ( λ ( i ) t ) k k ! k = 0 , 1 ,... (b) For a process to have independent increments, the events { N t + s − N t } and { N t } should be independent. In this case, it suffices to show that P ( N t + s − N t = k 1  N t = k 2 ) negationslash = P ( N t + s − N t = k 1 ) P ( N t + s − N t = k 1  N t = k 2 ) = P ( N t + s − N t = k 1 and N t = k 2 ) P ( N t = k 2 ) Since the process in question is no longer a simple Poisson process, we can’t assume that the two events in the numerator are independent. However, conditioned on the the event { X = i } , the process is Poisson. Hence, we can use the law of total probability to evaluate the numerator just as we did in part ( a ). P ( N t + s − N t = k 1 and N t = k 2 ) = summationdisplay i ∈ E P ( N t + s − N t = k 1 and N t = k 2  X = i ) P ( X = i ) = summationdisplay i ∈ E π ( i ) P ( N t + s ( i ) − N t ( i ) = k 1 ) P ( N t ( i ) = k 2 ) = summationdisplay i ∈ E π ( i ) e λ ( i )...
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 Fall '11
 SerapSavari
 Poisson Distribution, Probability theory, lim, Poisson process, Lj

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