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as the government tightened its scal policy Although GDP growth is primarily attributed to oil and natural gas, Russia s private sector, including wholesale, retail, banking, insurance, transportation, and communications, has grown over 10 percent in the last eight years As the Russian economy attempts to diversify the government is still overwhelmingly reliant on oil for revenues it will test several methods, such as special economic zones and state-run investment funds, to try to lure uneasy investors back to the region Economists have predicted a 7 percent rate of growth for Russia in 2007, which, if met, would make the nation the fastest growing among the G8 countries Since 1998, Russia s GDP has jumped 37 percent, an astonishing gure given the circumstances

14 Alina Tugend, Blackberry Jam; Government Executive November 1, 2004 15 Ben Worthen, BlackBerrys Ruin Vacations, Sick Days Wall Street Journal, Sept 26, 2008

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[2] C Alexopoulos and S-H Kim Output data analysis for simulations In E Y cesan, u C-H Chen, JL Snowdon, and JM Charnes, eds, Proceedings of the 2002 Winter Simulation Conference, pp 85 96 IEEE Press, 2002 [3] C Alexopoulos and BC Shultes Estimating reliability measures for highly-dependable Markov systems, using balanced likelihoos ratios IEEE Transactions on Reliability, 50(3): 265 280, 2001 [4] S Asmussen and R Rubinstein Sensitivity analysis of insurance risk models via simulation Management Science, 45: 1125 1141, 1999 [5] H Cancela, G Rubino, and B Tuf n MTTF estimation by Monte Carlo methods using Markov models Monte Carlo Methods and Applications, 8(4): 312 341, 2002 [6] H Cancela, G Rubino, and M Urquhart Path set based conditioning for transient simulation of highly dependable networks In Proceedings of the 7th IFAC Symposium on Cost Oriented Automation, 2004 [7] JA Carrasco Failure distance based on simulation of repairable fault tolerant systems Proceedings of the 5th International Conference on Modeling Techniques and Tools for Computer Performance Evaluation, pp 351 365, 1992 [8] PT De Boer, P L Ecuyer, G Rubino, and B Tuf n Estimating the probability of a rare event over a nite horizon In Proceedings of the 2007 Winter Simulation Conference IEEE Press, 2007 [9] V Demers, P L Ecuyer, and B Tuf n A combination of randomized quasi-Monte Carlo with splitting for rare-event simulation In Proceedings of the 2005 European Simulation and Modeling Conference, pp 25 32, Ghent, Belgium, 2005 EUROSIS [10] A Goyal, P Heidelberger, and Shahabuddin P Measure speci c dynamic importance sampling for availability simulations In Proceedings of the 1987 Winter Simulation Conference, pp 351 357, 1987 [11] A Goyal, P Shahabuddin, P Heidelberger, VF Nicola, and PW Glynn A uni ed framework for simulating Markovian models of highly reliable systems IEEE Transactions on Computers, C-41: 36 51, 1992 [12] P Heidelberger Fast simulation of rare events in queueing and reliability models In L Donatiello and R Nelson, eds, Performance Evaluation of Computer and Communication Systems, Lecture Notes in Computer Science 729, pp 165 202 Springer, New York, 1993 [13] P Heidelberger Fast simulation of rare events in queueing and reliability models ACM Transactions on Modeling and Computer Simulation, 5(1): 43 85, 1995 [14] P Heidelberger, VF Nicola, and P Shahabuddin Simultaneous and ef cient simulation of highly dependable systems with different underlying distributions In Proceedings of the 1992 Winter Simulation Conference, pp 458 465 IEEE Press, 1992 [15] P Heidelberger, P Shahabuddin, and VF Nicola Bounded relative error in estimating transient measures of highly dependable non-Markovian systems ACM Transactions on Modeling and Computer Simulation, 4(2): 137 164, 1994 [16] DL Iglehart and R Schassberger Discrete time methods for simulating continuous time Markov chains Advances in Applied Probability, 8: 772 788, 1976.

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