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The optimal claim limits follow by minimizing the function g( 1 , , 4 ) with respect to the parameters 1 , , 4 Ef cient numerical procedures are widely available to minimize a function of several variables Table 331 gives for a number of examples the optimal claim limits 1 , , 4 together with the minimal average In all examples we take cost g P1 = 10 000, r1 = 1500, P2 = 7500, r2 = 1000, P3 = 6000, r3 = 750, P4 = 5000, r4 = 500

company on He had the kind of values that last You ve probably seen the Walgreen Creed, something he wrote years ago Well, we read it at our store managers meetings, and we get goose bumps It s that good 44

The average damage size is 5000 in each example; the squared coef cient of 2 variation of the damage size D takes three values: cD = 1, 4 and 25 To see the effect of the shape of the probability density of the damage size on the claim limits, we take the gamma distribution and the lognormal distribution both having the same rst two moments In particular, the minimal average cost becomes increasingly 2 sensitive to the distributional form of the damage size D when cD gets larger Can you explain why the minimal average cost per year decreases when the variability of the claims increases

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In this section it is assumed that the Markov chain {Xn } satis es Assumption 331 The Markov chain then has a unique equilibrium distribution { j , j I } The j

k pkj ,

(341)

(342)

PDP context pro les Pro le 3 (high-speed corporate LAN access) APN = HighSpeedLanAccessmyCompany QoS = 60 kbyte/s Pro le 4 (low-speed corporate LAN access) APN = LowSpeedLanAccessmyCompany QoS = 40 kbyte/s

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In Section 341 we consider the case of a nite space I and discuss several methods to compute the equilibrium probabilities j The in nite-state model is dealt with in Section 342 It is shown that brute-force truncation is not necessary to get a nite system of linear equations when the state space I = {0, 1, } and the state probabilities j exhibit a geometric tail behaviour as j For this situation, which naturally arises in many applications, an elegant computational method for the state probabilities can be given Markov chains with a multidimensional state space are prevalent in stochastic networks and in such applications it often happens that the equilibrium probabilities are known up to a multiplicative constant If the number of states is too large for a direct computation of the multiplicative constant, the Metropolis Hastings algorithm and the Gibbs sampler may be used to obtain the equilibrium probabilities These powerful methods are discussed in Section 343 341 Methods for a Finite-State Markov Chain In general there are two methods to solve the Markov chain equations: (a) direct methods, (b) iterative methods To discuss these methods, let us assume that the states of the Markov chain are numbered or renumbered as 1, , N Direct methods A convenient direct method is a Gaussian elimination method such as the Gauss Jordan method This reliable method is recommended as long as the dimension N of the system of linear equations does not exceed the order of thousands The computational effort of Gaussian elimination is proportional to N 3 Reliable and ready-to-use codes for Gaussian elimination methods are widely available A Gaussian elimination method requires that the whole coef cient matrix is stored, since this matrix must be updated at each step of the algorithm This explains why a Gaussian elimination method suffers from computer memory problems when N.

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