Exponential Distribution & Poisson Process

1. Exponential Distribution & Poisson Process Memorylessness & other exponential distribution properties; Poisson process and compound P.P.’s 1

2. f(t) = { e –t, t³ 0 0, t < 0 t Pr{T£t) = ò e –u du = 1 – e –t (t³ 0) 0 1 E[T] =  1 2 Exponential Distribution: Basic Facts PDF CDF Mean Variance Var[T] = 2

3. Key Property: Memorylessness Memoryless Property Pr{Ta + b | Tb} = Pr{Ta} a, b 0 • Reliability: Amount of time a component has been in service has no effect on the amount of time until it fails • Inter-event times: Amount of time since the last event contains no information about the amount of time until the next event • Service times: Amount of remaining service time is independent of the amount of service time elapsed so far 3

4. If X1 and X2 are independent exponential r.v.’s with parameters (rate) l1 and l2 respectively, then P(x1< x2) = l1/(l1+l2) That is, the probability X1 occurs before X2 is l1/(l1+l2) Properties of Exponential Distribution Minimum of Two Exponentials: If X1, X2 , …, Xn are independent exponential r.v.’s where Xn has parameter (rate) li, then min(X1, X2 , …, Xn) is exponential with parameter (rate) l1 + l2 + … + ln Competing Exponentials: 4

5. Properties of Exponential RV. The probability of 1 event happening in the next t is Pr{T£ t ) = 1- e – t = 1 – { 1 + (– t )+ (– t )n/n! ) } When t is small, (– t )n 0 =  t Exponential is the only r.v. that has this property. 5

6. Counting Process A stochastic process {N(t), t  0} is a counting process if N(t) represents the total number of events that have occurred in [0, t] Then { N(t), t  0 } must satisfy: a) N(t)  0 b) N(t) is an integer for all t c)If s < t, then N (s)  N(t) and d) For s < t, N (t ) - N (s) is the number of events that occur in the interval (s, t ]. 6

7. Stationary & Independent Increments independent increments A counting process has independent increments if for any 0  s  t  u  v, N(t) – N(s) is independent of N(v) – N(u) i.e., the numbers of events that occur in non-overlapping intervals are independent r.v.s stationary increments A counting process has stationary increments if the distribution if, for any s < t, the distribution of N(t) – N(s) depends only on the length of the time interval, t – s. 7

8. Poisson Process Definition 1 A counting process {N(t), t  0} is a Poisson process with ratel, l > 0, if N(0) = 0 The process has independent increments The number of events in any interval of length t follows a Poisson distribution with mean t Pr{ N(t+s) – N(s) = n } = (t)ne –t/n! , n = 0, 1, . . . Where  is arrival rate and t is length of the interval Notice, it has stationary increments 8

9. Poisson Process Definition 2 A function f is said to be o(h) (“Little oh of h”) if A counting process {N(t), t  0} is a Poisson process with ratel, l > 0, if N(0) = 0 and the process has stationary and independent increments and Pr { N(h) = 1} = h + o(h) Pr { N(h)  1} =o(h) Definitions 1 and 2 are equivalent because exponential distribution is the only variable has this property 9

10. N(t) S1 S2 S3 S4 t T1 T2 T3 T4 Interarrival and Waiting Times The times between arrivals T1, T2, … are independent exponential r.v.’s with mean 1/: P(T1>t) = P(N(t) =0) = e -t The (total) waiting time until the nth event has a gamma distribution 10

11. Poisson Process – Pure Birth Process Pure Birth Process – Poisson Process Poisson Process Rate Matrix 11

12. An Example Suppose that you arrive at a single teller bank to find five other customers in the bank. One being served and the other four waiting in line. You join the end of the line. If the service time are all exponential with rate 5 minutes. What is the prob. that you will be served in 10 minutes ? What is the prob. that you will be served in 20 minutes ? What is the expected waiting time before you are served? 12

13. Other Poisson Process Properties Poisson Splitting: Suppose {N(t), t  0} is a P.P. with rate , and suppose that each time an event occurs, it is classified as type I with probability p and type II with probability 1-p, independently of all other events. Let N1(t) and N2(t), respectively, be the number of type I and type II events up to time t. Then {N1(t), t  0} and {N2(t), t  0} are independent Poisson processes with respective rates p and (1-p). 13

14. Other Poisson Process Properties Competing Poisson Processes: Suppose {N1(t), t  0} and {N2(t), t  0} are independent P.P. with respective rates l1 and l2. Let Sni be the time of the nth event of process i, i = 1,2. 14

15. Other Poisson Process Properties If Y1, Y2, …, Yn are random variables, then Y(1), Y(2), …, Y(n) are their order statistics if Y(k) is the kth smallest value among Y1, Y2, …, Yn, k = 1, …, n. Conditional Distribution of Arrival Times: Suppose {N(t), t  0} is a Poisson process with rate l and for some time t we know that N(t) = n. Then the arrival times S1, S2, …, Sn have the same conditional distribution as the order statistics of n independent uniform random variables on (0, t). 15

16. Compound Poisson Process A counting process {X(t), t  0} is a compoundPoisson process if: where {N(t), t  0} is a Poisson process and {Yi, i = 1, 2, …} are independent, identically distributed r.v.’s that are independent of {N(t), t  0}. By conditioning on N(t), we can obtain 16