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[17] S Juneja and P Shahabuddin In Proceedings of the 22nd International Symposium on Fault-Tolerant Computing, pp 150 159 IEEE Computer Society Press, 1992 [18] S Juneja and P Shahabuddin Fast simulation of Markov chains with small transition probabilities Management Science, 47(4): 547 562, 2001 [19] S Juneja and P Shahabuddin Rare event simulation techniques: An introduction and recent advances In SG Henderson and BL Nelson, eds, Simulation, Handbooks in Operations Research and Management Science, pp 291 350 Elsevier, Amsterdam, 2006 [20] J Keilson Markov Chain Models Rarity and Exponentiality Springer, New York, 1979 [21] P L Ecuyer and B Tuf n Splitting with weight windows to control the likelihood ratio in importance sampling In Proceedings of ValueTools 2006: International Conference on Performance Evaluation Methodologies and Tools, Pisa, Italy, 2006 ACM Publications [22] P L Ecuyer and B Tuf n Effective approximation of zero-variance simulation in a reliability setting In Proceedings of the 2007 European Simulation and Modeling Conference, pp 48 54, Ghent, Belgium, 2007 EUROSIS [23] EE Lewis and F B hm Monte Carlo simulation of Markov unreliability models o Nuclear Engineering and Design, 77: 49 62, 1984 [24] MK Nakayama General conditions for bounded relative error in simulations of highly reliable Markovian systems Technical Report RC 18993, IBM Research Division, T J Watson Research Center, Yorktown Heights, NY, 1993 [25] MK Nakayama A characterization of the simple failure biasing method for simulations of highly reliable Markovian systems ACM Transactions on Modeling and Computer Simulation, 4(1): 52 88, 1994 [26] MK Nakayama Asymptotics of likelihood ratio derivatives estimators in simulations of highly reliable Markovian systems Management Science, 41(3): 524 554, 1995 [27] MK Nakayama General conditions for bounded relative error in simulations of highly reliable Markovian systems Advances in Applied Probability, 28: 687 727, 1996 [28] MK Nakayama On derivative estimation of the mean time to failure in simulations of highly reliable Markovian systems Operations Research, 46: 285 290, 1998 [29] MK Nakayama, A Goyal, and PW Glynn Likelihood ratio sensitivity analysis for Markovian models of highly dependable systems Operations Research, 42(1): 137 157, 1994 [30] MK Nakayama and P Shahabuddin Likelihood ratio derivative estimation for nite-time performance measures in generalized semi-Markov processes Management Science, 44(10): 1426 1441, 1998 [31] MK Nakayama and P Shahabuddin Quick simulation methods for estimating the unreliability of regenerative models of large highly reliable systems Probability in the Engineering and Information Sciences, 18: 339 368, 2004 [32] VF Nicola, P Heidelberger, and P Shahabuddin Uniformization and exponential transformation: Techniques for fast simulation of highly dependable non-Markovian systems In Proceedings of the 22nd International Symposium on Fault-Tolerant Computing, pp 130 139 IEEE Computer Society Press, 1992. vb.net code 128 reader VB . NET Barcode Reader - How to Scan & Read Barcode in VB . NET ...
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