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Granted, it sounds a bit Eddie Haskel esque, were it not for Mr Iltis s sincerity He also had the numbers to back him up and plenty of small store affiliates who agreed with him When Agency head Raymond E Walker stepped down in 1950, his baby had over a thousand affiliates in all 48 states16 The Agency system accomplished everything it had been designed to do, and then some It spread the Walgreens name coast-to-coast; it saved small stores nationwide; it brought in some additional revenue for the company; and, under the category of unintended consequences, it also produced dozens of future stars who would become leaders in the decades to come though not all of them in the drugstore industry Walgreens Agency affiliates included a humble outlet in Huron, South Dakota, owned by Hubert Horatio Humphrey Sr His son Hubert worked there as a clerk during high school and later as a pharmacist before embarking on his political career, which led to the US Senate and to the White House as Lyndon Johnson s vice president That store was the meeting place for the whole town, Hubert Humphrey recalled in the 1960s, when his brother Ralph was the manager During the Depression, Dad canceled all his customers debts because he said they didn t have any money anyway People would trade chickens and eggs and meat for drugs, and we used the food at our luncheonette 25 cents for a full meal 17 The program would reach its peak in the late 1960s with some 1,900 Agency stores, about eight times more than Walgreens owned itself at the time This division was an important part of establishing the Walgreens name across the country, said John Rubino, who headed up the system in the 1970s Mr Walgreen s 1929 idea served our company well 18.

for this model Throughout this section it is assumed that the continuous-time Markov chain {X(t)} has a nite state space I 461 Transient Distribution of Cumulative Sojourn Times Consider a continuous-time Markov chain {X(t)} whose nite state space I is divided into two disjoint subsets I0 and If with I0 = the set of operational states and If = the set of failed states

The USSD signalling between HLR and gsmSCF is speci ed in CAMEL phase 2 Subscriber identi cation in the USSD information ows is done with IMSI That implies that the gsmSCF uses IMSI to identify a subscriber when it wants to initiate a USSD relationship with that subscriber The IMSI of a subscriber is reported to the gsmSCF during CAMEL service invocation Not all CAMEL services, however, use IMSI as subscriber identi cation Therefore, a CAMEL service

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De ne for any t > 0 the indicator random variable I (t) by I (t) = Then the random variable O(t) =

represents the cumulative operational time of the system during (0, t) The transient probability distribution of O(t) can be calculated by a nice probabilistic algorithm that is based on the uniformization method To nd P {O(t) x}, we rst uniformize the continuous-time Markov chain {X(t)} according to (453) Denoting by O(t) the cumulative operational time in the uniformized process {X(t)}, we have P {O(t) x} = P {O(t) x}, since the uniformized process is probabilistically equivalent with the original process By conditioning on the number of state transitions of the uniformized process during (0, t), we have

P {O(t) x} =

P {O(t) x | the uniformized process makes n state transitions in (0, t)} e t ( t)n n!

Subscriber data - O-CSI - T-CSI - CF data - MAP ATM req [set CF data] MAP ATM res gsmSCF addresses - < gsmSCF 1 > - < gsmSCF 2 > - < gsmSCF 3 >

The next key step in the analysis is the relation between the Poisson process and the uniform distribution In the uniformized process the epochs of the state transitions are determined by a Poisson process that is independent of the discretetime Markov chain governing the state transitions Under the condition that the uniformized process has made n state transitions during (0, t), the joint distribution of the epochs of these state transitions is the same as the joint distribution of the order statistics U (1) , , U (n) of n independent random variables U1 , , Un that are uniformly distributed on (0, t); see Theorem 115 Note that U (k) is the smallest

kth of the Ui The n state transitions in the interval (0, t) divide this interval into n + 1 intervals whose lengths are given by Y1 = U (1) , Y2 = U (2) U (1) , , Yn = U (n) U (n 1) and Yn+1 = t U (n) The random variables Y1 , , Yn+1 are obviously dependent variables, but they are exchangeable That is, for any permutation i1 , , in+1 of 1, , n + 1, P {Yi1 x1 , Yi2 x2 , , Yin+1 xn+1 } = P {Y1 x1 , Y2 x2 , , Yn+1 xn+1 } As a consequence P {Yi1 + + Yik x} = P {Y1 + + Yk x} for any sequence (Yi1 , , Yik ) of k interval lengths The probability distribution of Y1 + + Yk is easily given Let k n Then Y1 + + Yk = U (k) and so P {Y1 + + Yk x} = P {U (k) x} = P {at least k of the Ui are x}

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