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Years before, he had told her that once they had $20,000 in the bank they would buy a nice place in the country In March 1929, just months before the stock market crash, Charles purchased a gorgeous 15-acre tract of land outside Dixon, his hometown The lot was heavily wooded, with a clearing on the bluffs overlooking the Rock River On this clearing stood a log cabin, a large barn, a few smaller buildings, and all that remained of a once glorious mansion in woeful disrepair, a sort of postbellum Tara Almost a century earlier, about the time the Black Hawk War was fought in the same woods and fields, a man named Alexander Charters had bought and named the estate Hazelwood, in honor of a treasured park in Belfast, Northern Ireland, his former home Charters had opened Hazelwood to Abe Lincoln, Stephen A Douglas, and other dignitaries of the day until his death in 1878, after which the property gradually fell apart due to neglect Walgreen fulfilled one other promise, too On that brisk winter evening 22 years earlier, after the young couple had walked down the block to drop the final payment of the loan for his first store into the mail box, he had turned to his wife and whispered, One of these days, I ll buy you a fur coat, Myrtle Years later, Charles Walgreen provided his hard-working wife with everything she wanted The Walgreens dreams had come true It might have taken Walgreen 15 years to realize he was a pharmacist, but it only took 20 more to make Walgreens one of the largest drugstore chains in the world The Walgreen Way was spreading fast; and thanks to its foundation of bedrock values, it was built to last

j I,

independently of the initial state i The interested reader is referred to Chung (1967) for a proof The limiting probability pj can be interpreted as the probability that an outside observer nds the system in state j when the process has reached statistical equilibrium and the observer has no knowledge about the past evolution of the process The notion of statistical equilibrium relates not only to the length of time the process has been in operation but also to our knowledge of the past evolution of the system But a more concrete interpretation which better serves our purposes is that the long-run fraction of time the process will be in state j = pj with probability 1, (421)

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independently of the initial state X(0) = i More precisely, denoting for xed j the indicator variable Ij (t) by Ij (t) = it holds for any j I that 1 t t lim

smsSSF Subscriber sends SMS MSC sends SMS to SMSC SMS submission succeeds TC_Begin[IDP-SMS] TC_Continue[RT-SMS] TC_Continue[FCI-SMS; RRB-SMS; CUE-SMS] gsmSCF Perform credit check Reset Tssf Credit OK; continue SMS SMS submitted; Charging deduct credit system Close service SMS failed; release credit Close service Charging system Charging system

if X(t) = j, otherwise,

with probability 1,

independently of the initial state X(0) = i A proof of this result will be given in Section 43 using the theory of renewal-reward processes In Section 43 we also prove the following important theorem Theorem 421 Suppose the continuous-time Markov chain {X(t)} satis es Assumptions 412 and 421 Then the probabilities pj , j I are the unique solution to the linear equations j xj =

(422) (423)

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TC_Continue[ERB-SMS(O_SMS_Submitted)] TC_Continue[FCI-SMS; CUE-SMS]

in the unknowns xj , j I Moreover, let {xj , j I } be any solution to (422) with j xj < Then, for some constant c, xj = cpj for all j I The linear equations (422) are called the equilibrium equations or balance equations of the Markov process The equation (423) is a normalizing equation The probabilities pj are called the equilibrium probabilities of the continuous-time Markov chain They can be computed by solving a system of linear equations

Interpretation of the equilibrium equations A physical explanation of the equilibrium equations can be given by using the obvious principle that over the long run the average number of transitions out of state j per time unit = the average number of transitions into state j per time unit (424) Since pj is the long-run fraction of time the process is in state j and the leaving rate out of state j is j , it is intuitively obvious that the long-run average number of transitions out of state j per time unit = j pj Also, the following result will be intuitively obvious: the long-run average number of transitions from state k to state j per time unit = qkj pk (426) (425)

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