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Artykuły w czasopismach na temat „Rare events simulation”

Autor: Grafiati

Data publikacji: 12 października 2024

Data aktualizacji: 31 lipca 2025

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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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3

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Rubinstein, Reuven Y. "Optimization of computer simulation models with rare events." European Journal of Operational Research 99, no. 1 (1997): 89–112. http://dx.doi.org/10.1016/s0377-2217(96)00385-2.

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12

Frater, Michael R., Robert R. Bitmead, Rodney A. Kennedy, and Brian D. O. Anderson. "Fast simulation of rare events using reverse-time models." Computer Networks and ISDN Systems 20, no. 1-5 (1990): 315–21. http://dx.doi.org/10.1016/0169-7552(90)90040-y.

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13

Tsoucas, Pantelis. "Rare events in series of queues." Journal of Applied Probability 29, no. 1 (1992): 168–75. http://dx.doi.org/10.2307/3214800.

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In an ergodic network of K M/M/1 queues in series we consider the rare event that, as N increases, the total population in the network exceeds N during a busy period. By utilizing the contraction principle of large deviation theory, an action functional is obtained for this exit problem. The ensuing minimization is carried out for K = 2 and an indication is given for arbitrary K. It is shown that, asymptotically and for unequal service rates, the ‘most likely' path for this rare event is one where the arrival rate has been interchanged with the smallest service rate. The problem has been posed

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14

Tsoucas, Pantelis. "Rare events in series of queues." Journal of Applied Probability 29, no. 01 (1992): 168–75. http://dx.doi.org/10.1017/s0021900200106710.

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In an ergodic network of K M/M/1 queues in series we consider the rare event that, as N increases, the total population in the network exceeds N during a busy period. By utilizing the contraction principle of large deviation theory, an action functional is obtained for this exit problem. The ensuing minimization is carried out for K = 2 and an indication is given for arbitrary K. It is shown that, asymptotically and for unequal service rates, the ‘most likely' path for this rare event is one where the arrival rate has been interchanged with the smallest service rate. The problem has been posed

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15

Keller, Bettina G., and Peter G. Bolhuis. "Dynamical Reweighting for Biased Rare Event Simulations." Annual Review of Physical Chemistry 75, no. 1 (2024): 137–62. http://dx.doi.org/10.1146/annurev-physchem-083122-124538.

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Dynamical reweighting techniques aim to recover the correct molecular dynamics from a simulation at a modified potential energy surface. They are important for unbiasing enhanced sampling simulations of molecular rare events. Here, we review the theoretical frameworks of dynamical reweighting for modified potentials. Based on an overview of kinetic models with increasing level of detail, we discuss techniques to reweight two-state dynamics, multistate dynamics, and path integrals. We explore the natural link to transition path sampling and how the effect of nonequilibrium forces can be reweigh

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16

Chopra, Manan, Rohit Malshe, Allam S. Reddy, and J. J. de Pablo. "Improved transition path sampling methods for simulation of rare events." Journal of Chemical Physics 128, no. 14 (2008): 144104. http://dx.doi.org/10.1063/1.2889943.

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17

Heidelberger, Philip. "Fast simulation of rare events in queueing and reliability models." ACM Transactions on Modeling and Computer Simulation 5, no. 1 (1995): 43–85. http://dx.doi.org/10.1145/203091.203094.

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18

Rashki, Mohsen. "SESC: A new subset simulation method for rare-events estimation." Mechanical Systems and Signal Processing 150 (March 2021): 107139. http://dx.doi.org/10.1016/j.ymssp.2020.107139.

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19

Estecahandy, M., L. Bordes, S. Collas, and C. Paroissin. "Some acceleration methods for Monte Carlo simulation of rare events." Reliability Engineering & System Safety 144 (December 2015): 296–310. http://dx.doi.org/10.1016/j.ress.2015.07.010.

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20

Carter, E. A., Giovanni Ciccotti, James T. Hynes, and Raymond Kapral. "Constrained reaction coordinate dynamics for the simulation of rare events." Chemical Physics Letters 156, no. 5 (1989): 472–77. http://dx.doi.org/10.1016/s0009-2614(89)87314-2.

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21

de Koning, Maurice, Wei Cai, Babak Sadigh, Tomas Oppelstrup, Malvin H. Kalos, and Vasily V. Bulatov. "Adaptive importance sampling Monte Carlo simulation of rare transition events." Journal of Chemical Physics 122, no. 7 (2005): 074103. http://dx.doi.org/10.1063/1.1844352.

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22

Kim, Young Jin, Jae Jun Lee, and Julian Lee. "Gillespie Simulation of Rare Events in a Genetic Regulatory Network." Journal of the Korean Physical Society 74, no. 9 (2019): 907–11. http://dx.doi.org/10.3938/jkps.74.907.

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23

Borkar, V. S., S. Juneja, and A. A. Kherani. "Peformance Analysis Conditioned on Rare Events: An Adaptive Simulation Scheme." Communications in Information and Systems 3, no. 4 (2003): 259–78. http://dx.doi.org/10.4310/cis.2003.v3.n4.a3.

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24

Andersen, Lars Nørvang, Patrick J. Laub, and Leonardo Rojas-Nandayapa. "Efficient Simulation for Dependent Rare Events with Applications to Extremes." Methodology and Computing in Applied Probability 20, no. 1 (2017): 385–409. http://dx.doi.org/10.1007/s11009-017-9557-4.

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25

Vats, Shray, Raitis Bobrovs, Pär Söderhjelm, and Soumendranath Bhakat. "AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics." PLOS ONE 19, no. 8 (2024): e0307226. http://dx.doi.org/10.1371/journal.pone.0307226.

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Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by

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26

McLeish, Don L. "Bounded Relative Error Importance Sampling and Rare Event Simulation." ASTIN Bulletin 40, no. 1 (2010): 377–98. http://dx.doi.org/10.2143/ast.40.1.2049235.

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AbstractWe consider estimating tail events using exponential families of importance sampling distributions. When the cannonical sufficient statistic for the exponential family mimics the tail behaviour of the underlying cumulative distribution function, we can achieve bounded relative error for estimating tail probabilities. Examples of rare event simulation from various distributions including Tukey's g&h distribution are provided.

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27

Srinivasan, Rajan. "Importance Sampling - the Simulation Theory of Rare Events and its Applications ." Defence Science Journal 49, no. 1 (1999): 9–17. http://dx.doi.org/10.14429/dsj.49.3780.

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28

Melnik-Melnikov, P. G., and E. S. Dekhtyaruk. "Rare events probabilities estimation by “ Russian Roulette and Splitting ” simulation technique." Probabilistic Engineering Mechanics 15, no. 2 (2000): 125–29. http://dx.doi.org/10.1016/s0266-8920(97)00016-7.

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29

Hodkinson, Alexander, and Evangelos Kontopantelis. "Applications of simple and accessible methods for meta-analysis involving rare events: A simulation study." Statistical Methods in Medical Research 30, no. 7 (2021): 1589–608. http://dx.doi.org/10.1177/09622802211022385.

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Meta-analysis of clinical trials targeting rare events face particular challenges when the data lack adequate number of events and are susceptible to high levels of heterogeneity. The standard meta-analysis methods (DerSimonian Laird (DL) and Mantel–Haenszel (MH)) often lead to serious distortions because of such data sparsity. Applications of the methods suited to specific incidence and heterogeneity characteristics are lacking, thus we compared nine available methods in a simulation study. We generated 360 meta-analysis scenarios where each considered different incidences, sample sizes, betw

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30

Freitas, Ana Cristina Moreira, Jorge Milhazes Freitas, Mike Todd, and Sandro Vaienti. "Rare events for the Manneville–Pomeau map." Stochastic Processes and their Applications 126, no. 11 (2016): 3463–79. http://dx.doi.org/10.1016/j.spa.2016.05.001.

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31

Pienaar, Elsje. "Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems." Biomedical Engineering and Computational Biology 9 (January 2018): 117959721879025. http://dx.doi.org/10.1177/1179597218790253.

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Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that

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32

Asch, Anna, Ethan J. Brady, Hugo Gallardo, John Hood, Bryan Chu, and Mohammad Farazmand. "Model-assisted deep learning of rare extreme events from partial observations." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 4 (2022): 043112. http://dx.doi.org/10.1063/5.0077646.

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To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data are obtained from numerical simulations, as opposed to observations, with adequate samples from extreme events. However, to ensure the trained networks are applicable in practice, the training is not performed on the full simulation data; instead, we only use a small subset of observable quantities, which can be measured in practice. We investigate

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33

Abbot, Dorian S., Robert J. Webber, Sam Hadden, Darryl Seligman, and Jonathan Weare. "Rare Event Sampling Improves Mercury Instability Statistics." Astrophysical Journal 923, no. 2 (2021): 236. http://dx.doi.org/10.3847/1538-4357/ac2fa8.

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Abstract Due to the chaotic nature of planetary dynamics, there is a non-zero probability that Mercury’s orbit will become unstable in the future. Previous efforts have estimated the probability of this happening between 3 and 5 billion years in the future using a large number of direct numerical simulations with an N-body code, but were not able to obtain accurate estimates before 3 billion years in the future because Mercury instability events are too rare. In this paper we use a new rare-event sampling technique, Quantile Diffusion Monte Carlo (QDMC), to estimate that the probability of a M

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34

King, Leanna M., and Slobodan P. Simonovic. "A Deterministic Monte Carlo Simulation Framework for Dam Safety Flow Control Assessment." Water 12, no. 2 (2020): 505. http://dx.doi.org/10.3390/w12020505.

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Simulation has become more widely applied for analysis of dam safety flow control in recent years. Stochastic simulation has proven to be a useful tool that allows for easy estimation of the overall probability of dam overtopping failure. However, it is difficult to analyze “uncommon combinations of events” with a stochastic approach given current computing abilities, because (a) the likelihood of these combinations of events is small, and (b) there may not be enough simulated instances of these rare scenarios to determine their criticality. In this research, a Deterministic Monte Carlo approa

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35

Meyers, Timothy, Amine Stambouli, Karen McClure, and Daniel Brod. "Risk Assessment of Positive Train Control by Using Simulation of Rare Events." Transportation Research Record: Journal of the Transportation Research Board 2289, no. 1 (2012): 34–41. http://dx.doi.org/10.3141/2289-05.

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36

Hua, Bowen, Zhaohong Bie, Siu-Kui Au, Wenyuan Li, and Xifan Wang. "Extracting Rare Failure Events in Composite System Reliability Evaluation Via Subset Simulation." IEEE Transactions on Power Systems 30, no. 2 (2015): 753–62. http://dx.doi.org/10.1109/tpwrs.2014.2327753.

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37

Freitas, Ana Cristina Moreira, Jorge Milhazes Freitas, and Jorge Valentim Soares. "Rare events for product fractal sets *." Journal of Physics A: Mathematical and Theoretical 54, no. 34 (2021): 345202. http://dx.doi.org/10.1088/1751-8121/ac16c6.

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38

Li, Xiaoou, and Gongjun Xu. "Uniformly efficient simulation for extremes of Gaussian random fields." Journal of Applied Probability 55, no. 1 (2018): 157–78. http://dx.doi.org/10.1017/jpr.2018.11.

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AbstractIn this paper we consider the problem of simultaneously estimating rare-event probabilities for a class of Gaussian random fields. A conventional rare-event simulation method is usually tailored to a specific rare event and consequently would lose estimation efficiency for different events of interest, which often results in additional computational cost in such simultaneous estimation problems. To overcome this issue, we propose a uniformly efficient estimator for a general family of Hölder continuous Gaussian random fields. We establish the asymptotic and uniform efficiency of the pr

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39

Chen, Bohan, Jose Blanchet, Chang-Han Rhee, and Bert Zwart. "Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Random Walks and Compound Poisson Processes." Mathematics of Operations Research 44, no. 3 (2019): 919–42. http://dx.doi.org/10.1287/moor.2018.0950.

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40

Shayesteh Zadeh, Armin, and Baron Peters. "Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances." Life 11, no. 6 (2021): 570. http://dx.doi.org/10.3390/life11060570.

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Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retai

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41

Elabd, Ali A., El-Sayed M. El-Rabaie, and Abdelaziz T. Shalaby. "Analysis of rare events effect on single-electronics simulation based on orthodox theory." Journal of Computational Electronics 14, no. 2 (2015): 604–10. http://dx.doi.org/10.1007/s10825-015-0694-0.

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42

Paul, Sanjib, Nisanth N. Nair, and Harish Vashisth. "Phase space and collective variable based simulation methods for studies of rare events." Molecular Simulation 45, no. 14-15 (2019): 1273–84. http://dx.doi.org/10.1080/08927022.2019.1634268.

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43

Budde, Carlos E., Pedro R. D’Argenio, Arnd Hartmanns, and Sean Sedwards. "An efficient statistical model checker for nondeterminism and rare events." International Journal on Software Tools for Technology Transfer 22, no. 6 (2020): 759–80. http://dx.doi.org/10.1007/s10009-020-00563-2.

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Abstract Statistical model checking avoids the state space explosion problem in verification and naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach, its runtime becomes excessive in the presence of rare events, and it cannot soundly analyse nondeterministic models. In this article, we present : a statistical model checker that combines fully automated importance splitting to estimate the probabilities of rare events with smart lightweight scheduler sampling to approximate optimal schedulers in nondeterministic models. As part of the Modest Toolset, it suppo

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44

Xiao, Chao, Yu Lou, Jie Liu, Yuan Zhao, and Yikang Tian. "Economic events and the volatility of government bill rates." PLOS ONE 17, no. 10 (2022): e0276345. http://dx.doi.org/10.1371/journal.pone.0276345.

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Many studies show that in many countries (especially the G7), volatility in government bill rates far exceeds that in consumption growth rates. This volatility puzzle cannot be predicted by traditional disaster models, in which rare economic disasters are defined as a peak-to-trough percent fall in consumption (or real per capita GDP) by a high threshold (≥10%). For this purpose, we extend the traditional definition of rare economic disasters and propose a novel asset pricing model that models both good and bad events. We define a bad (or good) event as a peak-to-trough absolute decline (or a

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45

Emad, Ahmed, Seemi Ayub, Oumar Samassékou, et al. "Efficiency of Manual Scanning in Recovering Rare Cellular Events Identified by FluorescenceIn SituHybridization: Simulation of the Detection of Fetal Cells in Maternal Blood." Journal of Biomedicine and Biotechnology 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/610856.

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Fluorescencein situhybridization (FISH) and manual scanning is a widely used strategy for retrieving rare cellular events such as fetal cells in maternal blood. In order to determine the efficiency of these techniques in detection of rare cells, slides of XX cells with predefined numbers (1–10) of XY cells were prepared. Following FISH hybridization, the slides were scanned blindly for the presence of XY cells by different observers. The average detection efficiency was 84% (125/148). Evaluation of probe hybridization in the missed events showed that 9% (2/23) were not hybridized, 17% (4/23) w

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46

Abdel Menaem, Amir, Rustam Valiev, Vladislav Oboskalov, Taher S. Hassan, Hegazy Rezk, and Mohamed N. Ibrahim. "An Efficient Framework for Adequacy Evaluation through Extraction of Rare Load Curtailment Events in Composite Power Systems." Mathematics 8, no. 11 (2020): 2021. http://dx.doi.org/10.3390/math8112021.

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With the growing robustness of modern power systems, the occurrence of load curtailment events is becoming lower. Hence, the simulation of these events constitutes a challenge in adequacy indices assessment. Due to the rarity of the load curtailment events, the standard Monte Carlo simulation (MCS) estimator of adequacy indices is not practical. Therefore, a framework based on the enhanced cross-entropy-based importance sampling (ECE-IS) method is introduced in this paper for computing the adequacy indices. The framework comprises two stages. Using the proposed ECE-IS method, the first stage’s

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47

Kuwahara, Hiroyuki, and Ivan Mura. "An efficient and exact stochastic simulation method to analyze rare events in biochemical systems." Journal of Chemical Physics 129, no. 16 (2008): 165101. http://dx.doi.org/10.1063/1.2987701.

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48

Rjasanow, S., and W. Wagner. "Simulation of rare events by the stochastic weighted particle method for the Boltzmann equation." Mathematical and Computer Modelling 33, no. 8-9 (2001): 907–26. http://dx.doi.org/10.1016/s0895-7177(00)00289-2.

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49

Fuchigami, Sotaro. "Rare Events in Protein Simulation Revealed by using Time-Structure Based Independent Component Analysis." Biophysical Journal 104, no. 2 (2013): 170a. http://dx.doi.org/10.1016/j.bpj.2012.11.960.

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50

Zhang, Yuming, and Juan Ma. "A method of combined metamodel and subset simulation for reliability analysis of rare events." Advances in Engineering Software 195 (September 2024): 103693. http://dx.doi.org/10.1016/j.advengsoft.2024.103693.

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