Free Access
Issue |
ESAIM: PS
Volume 19, 2015
|
|
---|---|---|
Page(s) | 794 - 811 | |
DOI | https://doi.org/10.1051/ps/2015017 | |
Published online | 18 December 2015 |
- M. Amrein and H.R. Künsch, A variant of importance splitting for rare event estimation: Fixed number of successes. ACM Trans. Model. Comput. Simul. (TOMACS) (2011). [Google Scholar]
- S.-K. Au and J.L. Beck, Estimation of small failure probabilities in high dimensions by subset simulation. Probab. Eng. Mech. 16 (2001) 263–277. [CrossRef] [MathSciNet] [Google Scholar]
- A. Beskos, A. Jasra, N. Kantas and A. Thiery, On the convergence of adaptive sequential Monte Carlo methods. To appear in Ann. Appl. Probab. (2015). [Google Scholar]
- I. Bezáková, D. Štefankovic, V.V. Vazirani and E. Vigoda, Accelerating simulated annealing for the permanent and combinatorial counting problems. SIAM J. Comput. 37 (2008) 1429–1454. [CrossRef] [MathSciNet] [Google Scholar]
- Z.I. Botev and D.P. Kroese, An efficient algorithm for rare-event probability estimation, combinatorial optimization, and counting. Methodology Comput. Appl. Probab. 10 (2008) 471–505. [CrossRef] [Google Scholar]
- Z.I. Botev and D.P. Kroese, Efficient Monte Carlo simulation via the generalized splitting method. Stat. Comput. 22 (2012) 1–16. [CrossRef] [MathSciNet] [Google Scholar]
- C.-E. Brérhier, T. Leliévre and M. Rousset, Analysis of adaptive multilevel splitting algorithms in an idealized case. ESAIM: PS 19 (2015) 361–394. [CrossRef] [EDP Sciences] [Google Scholar]
- F. Cerou, P. Del Moral, A. Guyader and F. Malrieu, Fluctuation analysis of adaptive multilevel splitting. Preprint arXiv:1408.6366 (2014). [Google Scholar]
- F. Cérou and A. Guyader, Adaptive multilevel splitting for rare event analysis. Stoch. Anal. Appl. 25 (2007) 417–443. [CrossRef] [MathSciNet] [Google Scholar]
- F. Cérou, A. Guyader, R. Rubinstein and R. Vaisman, On the use of smoothing to improve the performance of the splitting method. Stochastic Models 27 (2011) 629–650. [CrossRef] [MathSciNet] [Google Scholar]
- F. Cérou, P. Del Moral, T. Furon and A. Guyader, Sequential Monte Carlo for rare event estimation. Stat. Comput. 22 (2012) 795–808. [CrossRef] [MathSciNet] [Google Scholar]
- B. Charles-Edouard, G. Maxime, G. Ludovic, L. Tony and R. Mathias, Unbiasedness of some generalized adaptive multilevel splitting algorithms. e-prints Preprint arXiv:1505.02674 (2015). [Google Scholar]
- P. Del Moral, A. Doucet and A. Jasra, Sequential Monte Carlo samplers. J. Roy. Statist. Soc.: Ser. B (Statistical Methodology) 68 (2006) 411–436. [Google Scholar]
- A. Froda, Sur la Distribution des Propriétés de Voisinage des Fonctions de Variables Réelles. Ph.D. thesis, Université de Paris (1929). [Google Scholar]
- M.J.J. Garvels, The splitting method in rare event simulation. Universiteit Twente (2000). [Google Scholar]
- P. Glasserman, P. Heidelberger, P. Shahabuddin and T. Zajic, Multilevel splitting for estimating rare event probabilities. Oper. Res. 4 (1999) 585–600. [CrossRef] [Google Scholar]
- A. Guyader, N. Hengartner and E. Matzner-Løber, Simulation and estimation of extreme quantiles and extreme probabilities. Appl. Math. Optim. 64 (2011) 171–196. [CrossRef] [MathSciNet] [Google Scholar]
- H. Hoos and T. Stiitzle, SATLlB: An online resource for research on SAT. Sat2000: highlights of satisfiability research in the year 2000 (2000) 283. [Google Scholar]
- M. Huber and S. Schott, Using TPA for Bayesian Inference. Bayesian Statistics 9 (2011) 257–282. [Google Scholar]
- H. Kahn and T.E. Harris, Estimation of Particle Transmission by Random Sampling. National Bureau of Standards Applied Mathematics Series 12 (1951) 27–30. [Google Scholar]
- M. Mitzenmacher and E. Upfal, Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press (2005). [Google Scholar]
- R. Motwani and P. Raghavan, Randomized Algorithms. Chapman & Hall/CRC (2010). [Google Scholar]
- M. Rosenbluth and A. Rosenbluth, Monte Carlo calculation of the average extension of molecular chains. J. Chem. Phys. 23 (2004) 356–359. [Google Scholar]
- R. Rubinstein, The Gibbs cloner for combinatorial optimization, counting and sampling. Methodol. Comput. Appl. Probab. 11 (2009) 491–549. [CrossRef] [MathSciNet] [Google Scholar]
- R. Rubinstein, Randomized algorithms with splitting: Why the classic randomized algorithms do not work and how to make them work. Methodol. Comput. Appl. Probab. 12 (2010) 1–50. [CrossRef] [MathSciNet] [Google Scholar]
- R. Rubinstein, A. Dolgin and R. Vaisman, The splitting method for decision making. Commun. Statistics-Simul. Comput. 41 (2012) 905–921. [CrossRef] [Google Scholar]
- R.Y. Rubinstein and D.P. Kroese, Simulation and the Monte Carlo method. In vol. 707. John Wiley & Sons (2011). [Google Scholar]
- E. Simonnet, Combinatorial analysis of the adaptive last particle method. To appear in Stat. Comput. (2014). Doi: 10.1007/s11222-014-9489-6. [Google Scholar]
- J. Skilling, Nested sampling for general bayesian computation. Bayesian Analysis 1 (2006) 833–859. [Google Scholar]
- C. Walter, Moving particles: A parallel optimal multilevel splitting method with application in quantiles estimation and meta-model based algorithms. Structural Safety 55 (2015) 10–25. [CrossRef] [Google Scholar]
- C. Walter, Point process-based Monte Carlo estimation. To appear in Stat. Comput. (2015) Doi: 10.1007/s11222-015-9617-y. [Google Scholar]
- C. Walter and G. Defaux, Rare event simulation: a point process interpretation with application in probability and quantile estimation. Proc. of the 12th International Conference on Applications of Statistics and Probability (2015). [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.