Open Access
Issue
ESAIM: PS
Volume 23, 2019
Page(s) 893 - 921
DOI https://doi.org/10.1051/ps/2019015
Published online 24 December 2019
  1. C.-E. Bréhier, T. Lelièvre and M. Rousset, Analysis of adaptive multilevel splitting algorithms in an idealized case. ESAIM: PS 19 (2015) 361–394. [CrossRef] [EDP Sciences] [Google Scholar]
  2. J. Bucklew, Introduction to rare event simulation. Springer Science & Business Media (2013). [Google Scholar]
  3. V. Caron, A. Guyader, M. Zuniga and B. Tuffin, Some recent results in rare event estimation. ESAIM: Proc. 44 (2014) 239–259. [Google Scholar]
  4. M. Čepin, Assessment of power system reliability: methods and applications. Springer Science & Business Media (2011). [Google Scholar]
  5. F. Cérou, P. Del Moral, F. Le Gland and P. Lezaud, Genetic genealogical models in rare event analy-sis. ALEA Latin Am. J. Probab. Math. Stat. 1 (2006) 181–203. [Google Scholar]
  6. F. Cérou, B. Delyon, A. Guyader and M. Rousset, On the asymptotic normality of adaptive multilevel splitting. SIAM/ASA J. Uncert. Quant. 7 (2019) 1–30. [Google Scholar]
  7. J.C. Chan, P.W. Glynn, D.P. Kroese et al., A comparison of cross-entropy and variance minimization strategies. J. Appl. Probab. 48 (2011) 183–194. [Google Scholar]
  8. H. Chraibi, Dynamic reliability modeling and assessment with PyCATSHOO: application to a test case. PSAM congress (2013). [Google Scholar]
  9. H. Chraibi, A. Dutfoy, T. Galtier and J. Garnier, Optimal input potential functions in the interacting particle system method. Preprint arXiv:1811.10450 (2018). [Google Scholar]
  10. H. Chraibi, J.-C. Houbedine and A. Sibler, Pycatshoo: Toward a new platform dedicated to dynamic reliability assessments of hybrid systems. PSAM congress (2016). [Google Scholar]
  11. M.H. Davis, Piecewise-deterministic Markov processes: A general class of non-diffusion stochastic models. J. Roy. Stat. Soc. Ser. B (Methodological) 46 (1984) 353–388. [Google Scholar]
  12. M.H. Davis, Markov Models & Optimization. In Vol. 49 of Monogaphs on Statistics and Applied Probability. CRC Press (1993). [Google Scholar]
  13. P.-T. de Boer, D.P. Kroese, S. Mannor and R.Y. Rubinstein, A tutorial on the cross-entropy method. Ann. Oper. Res. 134 (2005) 19–67. [Google Scholar]
  14. P. Del Moral and J. Garnier, Genealogical particle analysis of rare events. Ann. Appl. Probab. 15 (2005) 2496–2534. [Google Scholar]
  15. F. Dufour, H. Zhang and B. de Saporta, Numerical methods for simulation and optimization of piecewise deterministic Markov processes: application to reliability. John Wiley & Sons (2015). [Google Scholar]
  16. P. Dupuis and H. Wang, Importance sampling, large deviations, and differential games. Stochastics 76 (2004) 481–508. [Google Scholar]
  17. P. Heidelberger, Fast simulation of rare events in queueing and reliability models. ACM Trans. Model. Comput. Simul. 5 (1995) 43–85. [Google Scholar]
  18. I. Kuruganti, Importance sampling for markov chains: computing variance and determining optimal measures. In Proceedings of the 1996 Winter Simulation Conference. IEEE (1996) 273–280. [Google Scholar]
  19. P.-E. Labeau, A Monte-Carlo estimation of the marginal distributions in a problem of probabilistic dynamics. Reliab. Eng. Syst. Safety 52 (1996) 65–75. [Google Scholar]
  20. P.-E. Labeau, Probabilistic dynamics: estimation of generalized unreliability through efficient Monte-Carlo simulation. Ann. Nucl. Energy 23 (1996) 1355–1369. [Google Scholar]
  21. E. Lewis and F. Böhm, Monte-Carlo simulation of Markov unreliability models. Nucl. Eng. Des. 77 (1984) 49–62. [Google Scholar]
  22. M. Marseguerra and E. Zio, Monte-Carlo approach to psa for dynamic process systems. Reliab. Eng. Syst. Safety 52 (1996) 227–241. [Google Scholar]
  23. P. Metzner, C. Schütte and E. Vanden-Eijnden, Transition path theory for markov jump processes. Multis. Model. Simul. 7 (2009) 1192–1219. [Google Scholar]
  24. J. Morio, M. Balesdent, D. Jacquemart and C. Vergé, A survey of rare event simulation methods for static input–output models. Simul. Model. Pract. Theory 49 (2014) 287–304. [Google Scholar]
  25. M. Ramakrishnan, Unavailability estimation of shutdown system of a fast reactor by Monte-Carlo simulation. Ann. Nucl. Energy 90 (2016) 264–274. [Google Scholar]
  26. T. Rolski, H. Schmidli, V. Schmidt and J.L. Teugels, Stochastic processes for insurance and finance. In Vol. 505 of Wiley Series in Probability and Statistics. John Wiley & Sons (2009). [Google Scholar]
  27. D. Siegmund, Importance sampling in the Monte-Carlo study of sequential tests. Ann. Stat. 4 (1976) 673–684. [Google Scholar]
  28. N. Whiteley, A.M. Johansen and S. Godsill, Monte carlo filtering of piecewise deterministic processes. J. Comput. Graph. Stat. 20 (2011) 119–139. [Google Scholar]
  29. H. Zhang, F. Dufour, Y. Dutuit and K. Gonzalez, Piecewise deterministic Markov processes and dynamic reliability. Proc. Inst. Mech. Eng., Part O: J. Risk Reliab. 222 (2008) 545–551. [Google Scholar]
  30. E. Zio, The Monte-Carlo simulation method for system reliability and risk analysis. Springer (2013). [Google Scholar]
  31. P. Zuliani, C. Baier and E.M. Clarke, Rare-event verification for stochastic hybrid systems. In Proceedings of the 15th ACM international conference on Hybrid Systems: Computation and Control. ACM (2012) 217–226. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.