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Bibliografias temáticas / Rare events simulation

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Literatura científica selecionada sobre o tema "Rare events simulation"

Autor: Grafiati

Publicado: 12 de outubro de 2024

Última modificação: 31 de julho de 2025

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Artigos de revistas sobre o assunto "Rare events simulation"

1

Becker, M., and P. L. Douillet. "Hierarchical Simulation For Rare Events." International Journal of Modelling and Simulation 17, no. 2 (1997): 66–71. http://dx.doi.org/10.1080/02286203.1997.11760314.

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2

Lagnoux, Agnès. "RARE EVENT SIMULATION." Probability in the Engineering and Informational Sciences 20, no. 1 (2005): 45–66. http://dx.doi.org/10.1017/s0269964806060025.

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This article deals with estimations of probabilities of rare events using fast simulation based on the splitting method. In this technique, the sample paths are split into multiple copies at various stages in the simulation. Our aim is to optimize the algorithm and to obtain a precise confidence interval of the estimator using branching processes. The numerical results presented suggest that the method is reasonably efficient.

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Kubatur, Shruthi S., and Mary L. Comer. "Simulation of Rare Events in Images." Electronic Imaging 2018, no. 15 (2018): 227–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.15.coimg-227.

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4

Asmussen, Søren, Reuven Y. Rubinstein, and Chia-Li Wang. "Regenerative rare events simulation via likelihood ratios." Journal of Applied Probability 31, no. 3 (1994): 797–815. http://dx.doi.org/10.2307/3215157.

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In this paper we obtain some new theoretical and numerial results on estimation of small steady-state probabilities in regenerative queueing models by using the likelihood ratio (score function) method, which is based on a change of the probability measure. For simple GI/G/1 queues, this amounts to simulating the regenerative cycles by a suitable change of the interarrival and service time distribution, typically corresponding to a reference traffic intensity ρ0 which is < 1 but larger than the given one ρ. For the M/M/1 queue, the resulting gain of efficiency is calculated explicitly and s

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Asmussen, Søren, Reuven Y. Rubinstein, and Chia-Li Wang. "Regenerative rare events simulation via likelihood ratios." Journal of Applied Probability 31, no. 03 (1994): 797–815. http://dx.doi.org/10.1017/s0021900200045356.

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In this paper we obtain some new theoretical and numerial results on estimation of small steady-state probabilities in regenerative queueing models by using the likelihood ratio (score function) method, which is based on a change of the probability measure. For simple GI/G/1 queues, this amounts to simulating the regenerative cycles by a suitable change of the interarrival and service time distribution, typically corresponding to a reference traffic intensity ρ 0 which is &lt; 1 but larger than the given one ρ. For the M/M/1 queue, the resulting gain of efficiency is calculated explicitly

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Asmussen, Søren, Klemens Binswanger, Bjarne Højgaard, Soren Asmussen, and Bjarne Hojgaard. "Rare Events Simulation for Heavy-Tailed Distributions." Bernoulli 6, no. 2 (2000): 303. http://dx.doi.org/10.2307/3318578.

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Kabanov, A. A., and S. A. Dubovik. "Simulation of Rare Events in Stochastic Systems." Journal of Physics: Conference Series 2096, no. 1 (2021): 012151. http://dx.doi.org/10.1088/1742-6596/2096/1/012151.

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Abstract The paper presents algorithms for simulation rare events in stochastic systems based on the theory of large deviations. Here, this approach is used in conjunction with the tools of optimal control theory to estimate the probability that some observed states in a stochastic system will exceed a given threshold by some upcoming time instant. Algorithms for obtaining controlled extremal trajectory (A-profile) of the system, along which the transition to a rare event (threshold) occurs most likely under the influence of disturbances that minimize the action functional, are presented. It i

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Townsend, J. K., Z. Haraszti, J. A. Freebersyser, and M. Devetsikiotis. "Simulation of rare events in communications networks." IEEE Communications Magazine 36, no. 8 (1998): 36–41. http://dx.doi.org/10.1109/35.707815.

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Chambers, W. G. "Simulation of rare events in Gaussian processes." Electronics Letters 29, no. 15 (1993): 1384. http://dx.doi.org/10.1049/el:19930927.

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Bréhier, Charles-Edouard, Maxime Gazeau, Ludovic Goudenège, and Mathias Rousset. "Analysis and simulation of rare events for SPDEs." ESAIM: Proceedings and Surveys 48 (January 2015): 364–84. http://dx.doi.org/10.1051/proc/201448017.

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Teses / dissertações sobre o assunto "Rare events simulation"

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Liu, Hong. "Rare events, heavy tails, and simulation." Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3239435.

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2

Booth, Jonathan James. "New applications of boxed molecular dynamics : efficient simulation of rare events." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/13101/.

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This work presents Boxed Molecular Dynamics (BXD), an eﬃcient simulation tool for studying long time scale processes which are inaccessible to conventional methods of simulation. Boxed Molecular Dynamics is explained and introduced in the context of modelling the dynamics of proteins and peptides. Two major applications of Boxed Molecular Dynamics are reported. 1) - The mechanical unfolding of proteins induced by Atomic Force Microscopy methods is investigated. For the ﬁrst time, experimental data is reproduced and unfolding pathways are investigated without the use of high artiﬁcial pulling f

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3

Liu, Gang. "Rare events simulation by shaking transformations : Non-intrusive resampler for dynamic programming." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLX043/document.

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Cette thèse contient deux parties: la simulation des événements rares et le rééchantillonnage non-intrusif stratifié pour la programmation dynamique. La première partie consiste à quantifier des statistiques liées aux événements très improbables mais dont les conséquences sont sévères. Nous proposons des transformations markoviennes sur l'espace des trajectoires et nous les combinons avec les systèmes de particules en interaction et l'ergodicité de chaîne de Markov, pour proposer des méthodes performantes et applicables en grande généralité. La deuxième partie consiste à résoudre numériquement

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DHAMODARAN, RAMYA. "EFFICIENT ANALYSIS OF RARE EVENTS ASSOCIATED WITH INDIVIDUAL BUFFERS IN A TANDEM JACKSON NETWORK." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1099073321.

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Freitas, Rodrigo Moura 1989. "Molecular simulation = methods and applications = Simulações moleculares : métodos e aplicações." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/278440.

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Orientador: Maurice de Koning<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin<br>Made available in DSpace on 2018-08-23T00:50:21Z (GMT). No. of bitstreams: 1 Freitas_RodrigoMoura_M.pdf: 11496259 bytes, checksum: 41c29f22d80da01064cf7a3b9681b05f (MD5) Previous issue date: 2013<br>Resumo: Devido aos avanços conceptuais e técnicos feitos em física computacional e ciência dos materiais computacional nos estamos aptos a resolver problemas que eram inacessíveis a alguns anos atrás. Nessa dissertação estudamos a evolução de alguma destas técnicas, ap

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Hewett, Angela Dawn. "Expecting the unexpected : to what extent does simulation help healthcare professionals prepare for rare, critical events during childbearing?" Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/15431/.

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Pregnancy and childbirth presents both rare and critical events for which healthcare professionals are required to acquire and maintain competent clinical skills. In theory, a skill demonstrated using simulation will transfer into practice competently and confidently; the strength of simulation appears to lie in its validity with clinical context. Evidence shows that some professionals have difficulty responding appropriately to unexpected critical events and, therefore, there were two main aims: 1) to learn more about how healthcare practitioners develop skills in order to prepare for and res

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Rai, Ajit. "Estimation de la disponibilité par simulation, pour des systèmes incluant des contraintes logistiques." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S105/document.

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L'analyse des FDM (Reliability, Availability and Maintainability en anglais) fait partie intégrante de l'estimation du coût du cycle de vie des systèmes ferroviaires. Ces systèmes sont hautement fiables et présentent une logistique complexe. Les simulations Monte Carlo dans leur forme standard sont inutiles dans l'estimation efficace des paramètres des FDM à cause de la problématique des événements rares. C'est ici que l'échantillonnage préférentiel joue son rôle. C'est une technique de réduction de la variance et d'accélération de simulations. Cependant, l'échantillonnage préférentiel inclut

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Picciani, Massimiliano. "Rare events in many-body systems : reactive paths and reaction constants for structural transitions." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00706510.

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Cette thèse aborde l'étude de phénomènes physiques fondamentaux, avec des applications aux matériaux d'intérêt nucléaire. Nous avons développé des méthodes pour l'étude d'évènements rares concernant des transitions structurales thermiquement activées dans des systèmes à N-corps. La première méthode consiste en la simulation numérique du courant de probabilité associé aux chemins réactifs. Après avoir dérivé les équations d'évolution du courant de probabilité, on échantillonne ce courant grâce à un algorithme de type Monte Carlo Diffusif. Cette technique, dénommée Transition Current Sampling, a

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Silva, lopes Laura. "Méthodes numériques pour la simulation d'évènements rares en dynamique moléculaire." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1045.

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Dans les systèmes dynamiques aléatoires, tels ceux rencontrés en dynamique moléculaire, les événements rares apparaissent naturellement, comme étant liés à des fluctuations de probabilité faible. En dynamique moléculaire, le repliement des protéines, la dissociation protéine-ligand, et la fermeture ou l’ouverture des canaux ioniques dans les membranes, sont des exemples d’événements rares. La simulation d’événements rares est un domaine de recherche important en biophysique depuis presque trois décennies.En dynamique moléculaire, on est particulièrement intéressé par la simulation de la transi

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Saggadi, Samira. "Simulation d'évènements rares par Monte Carlo dans les réseaux hautement fiables." Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S055.

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Le calcul de la fiabilité des réseaux est en général un problème NP-difficile. On peut par exemple s’intéresser à la fiabilité des systèmes de télécommunications où l'on veut évaluer la probabilité qu’un groupe sélectionné de nœuds peuvent communiquer. Dans ce cas, un ensemble de nœuds déconnectés peut avoir des conséquences critiques, que ce soit financières ou au niveau de la sécurité. Une estimation précise de la fiabilité est ainsi nécessaire. Dans le cadre de ce travail, on s'intéresse à l’étude et au calcul de la fiabilité des réseaux hautement fiables. Dans ce cas la défiabilité est trè

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Livros sobre o assunto "Rare events simulation"

1

Shi, Yixi. Rare Events in Stochastic Systems: Modeling, Simulation Design and Algorithm Analysis. [publisher not identified], 2013.

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2

Bucklew, James Antonio. Introduction to Rare Event Simulation. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4078-3.

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3

Bucklew, James Antonio. Introduction to Rare Event Simulation. Springer New York, 2004.

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4

Rubino, Gerardo, and Bruno Tuffin, eds. Rare Event Simulation using Monte Carlo Methods. John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745403.

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5

1955-, Rubino Gerardo, and Tuffin Bruno, eds. Rare event simulation using Monte Carlo methods. Wiley, 2009.

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6

Lamers, Eugen. Contributions to Simulation Speed-Up: Rare Event Simulation and Short-Term Dynamic Simulation for Mobile Network Planning. Vieweg+Teubner / GWV Fachverlage GmbH, Wiesbaden, 2008.

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7

Allen, Michael P., and Dominic J. Tildesley. Rare event simulation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0010.

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The development of techniques to simulate infrequent events has been an area of rapid progress in recent years. In this chapter, we shall discuss some of the simulation techniques developed to study the dynamics of rare events. A basic summary of the statistical mechanics of barrier crossing is followed by a discussion of approaches based on the identification of reaction coordinates, and those which seek to avoid prior assumptions about the transition path. The demanding technique of transition path sampling is introduced and forward flux sampling and transition interface sampling are conside

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(Editor), Bruce J. Berne, Giovanni Cicotti (Editor), and David F. Coker (Editor), eds. Classical and Quantum Dynamics in Condensed Phase Simulations: Proceedings of the International School of Physics "Computer Simulation of Rare Events and ... Classical and Quantum Condensed-Phase syste. World Scientific Publishing Company, 1998.

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9

Bucklew, James A. Introduction to Rare Event Simulation. Springer, 2004.

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10

An introduction to rare event simulation. Springer, 2003.

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Capítulos de livros sobre o assunto "Rare events simulation"

1

Ciccotti, G. "Molecular Dynamics Simulations of Nonequilibrium Phenomena and Rare Dynamical Events." In Computer Simulation in Materials Science. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3546-7_6.

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2

Rubino, Gerardo. "Network Reliability, Performability Metrics, Rare Events and Standard Monte Carlo." In Advances in Modeling and Simulation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10193-9_20.

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3

Wainrib, Gilles. "Some Numerical Methods for Rare Events Simulation and Analysis." In Lecture Notes in Mathematics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32157-3_4.

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Asmussen, Søren. "Large Deviations in Rare Events Simulation: Examples, Counterexamples and Alternatives." In Monte Carlo and Quasi-Monte Carlo Methods 2000. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56046-0_1.

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Pelikan, Martin, Jiri Ocenasek, Simon Trebst, Matthias Troyer, and Fabien Alet. "Computational Complexity and Simulation of Rare Events of Ising Spin Glasses." In Genetic and Evolutionary Computation – GECCO 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_4.

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6

Carney, Meagan, Holger Kantz, and Matthew Nicol. "Analysis and Simulation of Extremes and Rare Events in Complex Systems." In Advances in Dynamics, Optimization and Computation. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51264-4_7.

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D’Argenio, Pedro R., Carlos E. Budde, Matias David Lee, Raúl E. Monti, Leonardo Rodríguez, and Nicolás Wolovick. "The Road from Stochastic Automata to the Simulation of Rare Events." In ModelEd, TestEd, TrustEd. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68270-9_14.

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van Moorsel, Aad P. A., Boudewijn R. Haverkort, and Ignas G. Niemegeers. "Fault Injection Simulation: A Variance Reduction Technique for Systems with Rare Events." In Dependable Computing for Critical Applications 2. Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-9198-9_6.

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Zimmermann, Armin. "Extended Reward Measures in the Simulation of Embedded Systems With Rare Events." In Embedded Systems – Modeling, Technology, and Applications. Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-4933-1_5.

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Puch, Stefan, Martin Fränzle, and Sebastian Gerwinn. "Quantitative Risk Assessment of Safety-Critical Systems via Guided Simulation for Rare Events." In Leveraging Applications of Formal Methods, Verification and Validation. Verification. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03421-4_20.

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Trabalhos de conferências sobre o assunto "Rare events simulation"

1

Christianen, Mark, Henry Lam, Maria Vlasiou, and Bert Zwart. "Importance Sampling of Rare Events for Distribution Networks with Stochastic Loads." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838841.

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2

Edjinedja, Kokou, Omar Elfahim, Émilien Arnaud, Oussama Barakat, Thibaut Desmettre, and Stephan Robert-Nicoud. "Multi-Agent Modeling and Discrete-Event Simulation for Analyzing the Responsiveness of Pre-Hospital Emergency Services in the Event of Mass Casualties Due to Rare Events." In 2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE, 2024. https://doi.org/10.1109/ictmod63116.2024.10878164.

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Bhattacharjee, Anirban, and Sandeep Juneja. "Selecting the Safest Design in Rare Event Settings." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838970.

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Hult, Henrik, Aastha Jain, Sandeep Juneja, Pierre Nyquist, and Sushant Vijayan. "A Deep Learning Approach for Rare Event Simulation in Diffusion Processes." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838791.

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Foo, Jasmine, and Kevin Leder. "Rare events in cancer recurrence timing." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465239.

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Kim, Youngjun, Yonatan Gur, and Mykel J. Kochenderfer. "Heuristics for planning with rare catastrophic events." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248024.

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Gordon, Steven, and David Garbin. "Of dual-core networks during rare events." In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6148017.

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Fresnedo, R. D. "Quick simulation of rare events in networks." In the 21st conference. ACM Press, 1989. http://dx.doi.org/10.1145/76738.76805.

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Wang, Xingyu, and Chang-Han Rhee. "Rare-Event Simulation for Multiple Jump Events in Heavy-Tailed Lévy Processes with Infinite Activities." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9383865.

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Estecahandy, Maider, Laurent Bordes, Stephane Collas, and Christian Paroissin. "Acceleration methods for Monte Carlo simulation of rare events." In 2015 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2015. http://dx.doi.org/10.1109/rams.2015.7105098.

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Relatórios de organizações sobre o assunto "Rare events simulation"

1

Washio, Takashi. Model Learning for Probabilistic Simulation on Rare Events and Scenarios. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada616937.

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2

Kollman, Craig. Rare event simulation in radiation transport. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10172053.

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3

Sarupria, Sapna, Steven Hall, and Ryan DeFever. Sampling Rare Events In Aqueous Systems Using Molecular Simulations. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2376138.

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4

Shortle, John F. New Approaches for Rare-Event Simulation and Decision Making. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1128906.

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Martin, S., Larry Daggett, Morgan Johnston, et al. Houston Ship Channel Expansion Improvement Project – Navigation Channel Improvement Study : ship simulation results. Coastal and Hydraulics Laboratory (U.S.), 2021. http://dx.doi.org/10.21079/11681/42342.

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In 2020, the US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory, provided technical oversight during a navigation study to assist the Galveston District evaluation of different channel widening alternatives for larger ships transiting the Houston Ship Channel (HSC), Texas. The widening proposals encompassed several areas of the HSC including the Bay Section, the Bayport Ship Channel, Barbours Cut Channel, and the Bayou Section. The study was performed at the San Jacinto College Maritime Technology and Training Center (SJCMTTC) Ship/Tug Simulator (STS) Fa

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6

Dean, Thomas, and aul Dupuis. Splitting for Rare Event Simulation: A Large Deviations Approach to Design and Analysis. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada476257.

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Wallace, Adam. Towards The Development of Rare Event Simulation Methods For Improved Mechanistic Understanding of Mineral Surface Reactivity. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2447356.

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Kushner, Harold, and Paul Dupuis. Stochastic Control and Numerical Methods with Applications to Communications. Game Theoretic/Subsolution to Importance Sampling for Rare Event Simulation. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada499989.

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9

Debuque-Gonzales, Margarita, and John Paul Corpus. Let's Get Fiscal: Extending the Small Macroeconometric Model of the Philippine Economy. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.43.

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This study presents a small macroeconometric model with a fiscal sector, extending the model presented in Debuque-Gonzales and Corpus (2022). The model retains the original core blocks of domestic demand, international trade, employment, prices, and monetary sectors and adds a fiscal sector consisting of equations for government revenues, expenditures, and debt. Behavioral equations are estimated in error-correction form (using ARDL methodology) on quarterly data from 2002 to 2019. In-sample simulations demonstrate acceptable levels of predictive accuracy for most macroeconomic variables, even

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Edeh, Henry C. The Effect of the Liberian Government’s Taxation Policies on Poverty and Inequality. Institute of Development Studies, 2024. http://dx.doi.org/10.19088/ictd.2024.102.

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Many low- and middle-income countries engage in tax policy reforms. This is usually motivated by efforts to increase tax revenue collection, and also sometimes to improve equity by reducing the burden of taxation on relatively poorer taxpayers. However, little is known about the extent to which these tax policy reforms affect poverty and inequality, especially at the household level. This study exhaustively analyses the progressivity and effect of taxation policies on poverty and inequality in Liberia, a low-income country, for the first time. After accounting for the informal sector, personal

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