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[33] VF Nicola, MK Nakayama, P Heidelberger, and A Goyal Fast simulation of dependability models with general failure, repair and maintenance processes In Proceedings of the 20th International Symposium on Fault-Tolerant Computing, pp 491 498 IEEE Computer Society Press, 1990 [34] VF Nicola, MK Nakayama, P Heidelberger, and A Goyal Fast simulation of highly dependable systems with general failure and repair processes IEEE Transactions on Computers, 42(12): 1440 1452, December 1993 [35] VF Nicola, P Shahabuddin, P Heidelberger, and PW Glynn Fast simulation of steady-state availability in non-Markovian highly dependable systems In Proceedings of the 23rd International Symposium on Fault-Tolerant Computing, pp 38 47 IEEE Computer Society Press, 1993 [36] C Papadopoulos A new technique for MTTF estimation in highly reliable Markovian systems Monte Carlo Methods and Applications, 4(2): 95 112, 1998 [37] A Ridder Importance sampling simulations of Markovian reliability systems using cross-entropy Annals of Operations Research, 143: 119 136, 2005 [38] P Shahabuddin Fast transient simulation of Markovian models of highly dependable systems Performance Evaluation, 20: 267 286, 1994 [39] P Shahabuddin Importance sampling for the simulation of highly reliable Markovian systems Management Science, 40(3): 333 352, 1994 [40] P Shahabuddin, VF Nicola, P Heidelberger, A Goyal, and PW Glynn Variance reduction in mean time to failure simulations In Proceedings of the 1988 Winter Simulation Conference, pp 491 499 IEEE Press, 1988 [41] B Tuf n Bounded normal approximation in simulations of highly reliable Markovian systems Journal of Applied Probability, 36(4): 974 986, 1999 [42] B Tuf n On numerical problems in simulations of highly reliable Markovian systems In Proceedings of the 1st International Conference on Quantitative Evaluation of Systems (QEST), pp 156 164 Los Alamitos, CA: IEEE Computer Society Press, 2004 [43] B Tuf n, W Sandmann, and P L Ecuyer Robustness properties in simulations of highly reliable systems In Proceedings of RESIM 2006 , University of Bamberg, Germany, October 2006.

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consumer market In India, the preferences, quotas, and tariffs prior to 1990 made it a virtually closed system with low ef ciency, low productivity, and poor living standards The new changes in India allow foreign entities at least 51 percent ownership in most sectors and up to 100 percent ownership in special urgently needed sectors In the WTO, Brazil has emerged as a leader of the G20 Group of Developing Countries, which includes India, China, and Mexico They are primarily concerned with agriculture and leveling the playing eld between developed and developing economies by negotiating for the removal of farming subsidies in the United States and the European Union Brazil has formed a powerful coalition for advancing the interests of trade liberalization and development objectives by joining forces with other developing nations Mexico s trade policies are among the most open in the world, with free trade agreements with the United States, Canada, the European Union, and many other countries It has abandoned price controls and encouraged competition through regulatory reform and export-led growth These changes have created a new dynamic for a worldwide exchange of goods and services that will result in lucrative opportunities for both the exporters and importers But what ultimately will matter is that these economies allow free and fair competition from outsiders and move beyond the protected enclaves of domestic companies despite extreme pressure to maintain such protection In China and India, the state-owned enterprises are entrenched both as a political force and an economic power However, the expectation is that it is a new day and open trade and its rewards will be seen as expansionary and the bene ts universal

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This chapter discusses Monte Carlo techniques for rare event simulation in the case of static models, that is, models in which time is not an explicit variable The main example and the one that will be used in the chapter is the network reliability analysis problem, where the models are graphs with probabilities associated with their components (with arcs or edges, and/or with nodes) Other typical names in this domain are fault trees, block diagrams, etc All these models are in general solved using combinatorial techniques, but only for quite small sizes, because their analysis is extremely costly in terms of computational resources The only methods able to deal with models having arbitrary size are Monte Carlo techniques, but there the main dif culty is with the rare event case, the focus of this chapter In many areas (eg, telecommunications, transportation systems, energy productions plants), either the components are very reliable or redundancy schemes are adopted, resulting in extremely reliable systems This means that a system s failure is (or should be) a rare event

16 Sharon Begley, Will the BlackBerry Sink the Presidency , Newsweek, Feb 16, 2009

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a metropolitan area In 1970, by contrast, that gure totaled 70 percent The nation s main cities, Moscow and St Petersburg, account for nearly 10 percent of its total population, with 104 million and 47 million citizens, respectively The number of people living in rural villages has slowly decreased since the early 1960s, as migration to Russia s cities increased with the demands of industrialization

Rare Event Simulation using Monte Carlo Methods Edited by Gerardo Rubino and Bruno Tuffin 2009 John Wiley & Sons, Ltd ISBN: 978-0-470-77269-0

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