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three times his stake with respective probabilities 025 and 015 His strategy is to bet $5 each time his payroll is more than $50 dollars and $10 otherwise De ne an appropriate Markov chain to compute the probability that Joe reaches his goal Also calculate the expected number of bets placed by Joe until he has gone broke or reached his goal 39 A training program consists of three parts, each having a length of one month Fifty percent of the starting students immediately pass the rst part after one month, 30% drop out before the end of the rst month and 20% take the rst part again Seventy percent of the last group pass the rst part after a second trial and the other 30% still drop out Eighty percent of the students taking the second part pass this second part after the rst trial, 10% drop out after the rst trial and the other 10% move on after a second trial of the rst part Any student streaming into the third part of the training program will complete it successfully Calculate the probability that a starting student will be successful 310 Consider a nite-state Markov chain {Xn } with no two disjoint closed sets The matrix of one-step transition probabilities is called doubly stochastic when for each column the sum of the column elements equals 1 Verify that the equilibrium distribution of such a Markov chain is a uniform distribution 311 A gambling device is tuned such that a player who wins (loses) on a given play will win on the next play with probability 025 (050) The player pays $1 for each play and receives $250 for each play that is won Use Markov chain analysis to nd out whether the game is fair or not 312 A factory has a storage tank with a capacity of 4 m3 for temporarily storing waste produced by the factory Each week the factory produces 0, 1, 2 or 3 m3 waste with respective probabilities p0 = 1 , p1 = 1 , p2 = 1 , and p3 = 1 If the amount of waste 8 2 4 8 produced in one week exceeds the remaining capacity of the tank, the excess is specially removed at a cost of $30 per cubic metre At the end of each week there is a regular opportunity to remove waste from the storage tank at a xed cost of $25 and a variable cost of $5 per cubic metre The following policy is used If at the end of the week the storage tank contains more than 2 m3 of waste, the tank is emptied; otherwise no waste is removed Use Markov chain analysis to nd the long-run average cost per week 313 In a series of repeated plays, you can choose each time between games A and B During each play you win $1 or you lose $1 You are also allowed to play when your capital is not positive (a negative capital corresponds to a debt) In game A there is a single coin This coin lands heads with probability 1 ( = 0005) and tails with probability 2 1 + In game B there are two coins One coin lands heads with probability 1 and 2 10 the other coin lands heads with probability 3 If you play game B, then you must take 4 the rst coin when your current capital is a multiple of 3 and you must take the other coin otherwise In each play of either game you win $1 if the coin lands heads and you lose $1 otherwise (a) Use Markov chain analysis to verify that the long-run fraction of plays you win is 04957 when you always play game B (Hint : a three-state Markov chain suf ces) (b) Suppose you alternately play the games A, A, B, B, A, A, B, B, Use an appropriate Markov chain to verify that the long-run fraction of plays you win is 05064 This problem shows that in special cases with dependencies, a combination of two unfavourable games may result in a favourable game This paradox is called Parrondo s paradox after the Spanish physicist Juan Parrondo 314 At the beginning of each day, a crucial piece of electronic equipment is inspected and then classi ed as being in one of the working conditions i = 1, , N Here the working condition i is better than the working condition i + 1 If the working condition is i = N the piece must be replaced by a new one and such an enforced replacement takes two days If the working condition is i with i < N there is a choice between preventively replacing.

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where (0, ) is the scale factor, controlling the amplication of the di erential variation Di erent approaches can be used to select the target vector and to calculate di erentials as iscussed in Section 132.The DE crossover operator implements a discrete recombination of the trial vector, ui (t), and the parent vector, xi (t), to produce o spring, xi (t) Crossover is implemented as follows: uij (t) if j J (133) xij (t) = xij (t) otherwise where xij (t) refers to the j-th element of the vector xi (t), and J is the set of element indices that will undergo perturbation (or in other words, the set of crossover points) Di erent methods can be used to determine the set, J , of which the following two approaches are the most frequently used [811, 813]: Binomial crossover: The crossover points are randomly selected from the set of possible crossover points, {1, 2, , nx }, where nx is the problem dimension Algorithm 131 summarizes this process In this algorithm, pr is the probability that the considered crossover point will be included The larger the value of pr , the more crossover points will be selected compared to a smaller value This means that more elements of the trial vector will e used to produce the o spring, and less of the parent vector Because a probabilistic decision is made as to the. Barcode In VB.NET Using Barcode encoder for .NET .Related: EAN-13 Generating .NET , .NET EAN 128 Generating , Generate UPC-A .NET

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Let us denote by v a sample from the distribution of V Then a sample Z (k) (G) of Z(G) can be deduced from a sample Z (k) (Gv ) of Z(Gv ) by Z (k) (G) = H + (1 pH )Z (k) (Gv ) If s and t are merged into a nal single node, the unreliability of Gv is equal to 0 and then Z (k) (G) = H + (1 pH ) 0 = H If s and t are not connected, the unreliability of Gv is equal to 1 and then Z (k) (G) = H + (1 pH ) 1 = 1 Otherwise, we have found an st-cut in Gv and we proceed again as before The main interest of this procedure is that Gv is smaller than G, and sometimes much smaller, because of the series parallel simpli cations, deletions and contractions performed A function which returns a trial of Z(G) can be summarized as follows: TRIAL-RVR(G, K) 1 Check end recursion condition: If |K| = 1 return(0) If G is not K-connected return(1) 2 Construct sp-red(G) by applying series parallel reductions to G 3 Find a K-cut in sp-red(G): = {l1 , , lH } 4 Compute the probability H that all links in are down 5 Compute the probability mass function distribution of the random variable V 6 Generate a trial v of V 7 Construct the network Gv = sp-red((G l1 l2 lv 1 ) lv ) 8 Recursive step: return( H + (1 H ) Trial-RVR(Gv , Kv )) The memory space complexity of the function TRIAL-RVR(G, K) is of order O(|E|(|E| + |V|)) and time complexity is, in the worst case, of order O(|E|(|K|2 |E||V|2 )) The worst case corresponds to a version using a maximal ow procedure in order to select a K-cut at step 3 of the above algorithm By calling the function TRIAL-RVR(G K) N times, we obtain N independent trials Z (k) (G) of Z(G), 1 k N The sample mean Z(G) of these trials leads to an estimate of Q = 1 R and the variance is estimated by VRV R 1 = N (N 1).

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