Bibliografías temáticas / Stochastic simulation

Literatura académica sobre el tema "Stochastic simulation"

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Publicado: 4 de junio de 2021
Última modificación: 8 de junio de 2024

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Artículos de revistas sobre el tema "Stochastic simulation"

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Balmer, David y Brian D. Ripley. "Stochastic Simulation". Journal of the Operational Research Society 40, n.º 2 (febrero de 1989): 201. http://dx.doi.org/10.2307/2583240.

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Nelson, Barry L. y Brian D. Ripley. "Stochastic Simulation." Journal of the American Statistical Association 84, n.º 405 (marzo de 1989): 334. http://dx.doi.org/10.2307/2289887.

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Morgan, B. J. T. y B. D. Ripley. "Stochastic Simulation." Biometrics 44, n.º 2 (junio de 1988): 628. http://dx.doi.org/10.2307/2531879.

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Balmer, David. "Stochastic Simulation". Journal of the Operational Research Society 40, n.º 2 (febrero de 1989): 201–2. http://dx.doi.org/10.1057/jors.1989.26.

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Booker, Jane M. "Stochastic Simulation". Technometrics 30, n.º 2 (mayo de 1988): 231–32. http://dx.doi.org/10.1080/00401706.1988.10488373.

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Bongiovanni, John. "Stochastic simulation". Environmental Software 3, n.º 1 (marzo de 1988): 45. http://dx.doi.org/10.1016/0266-9838(88)90009-3.

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Clarke, Michael D. y Brian D. Ripley. "Stochastic Simulation." Statistician 36, n.º 4 (1987): 430. http://dx.doi.org/10.2307/2348862.

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Junker, Brian W. y Brian D. Ripley. "Stochastic Simulation". Journal of Educational Statistics 16, n.º 1 (1991): 82. http://dx.doi.org/10.2307/1165101.

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Kemp, C. D. y B. D. Ripley. "Stochastic Simulation." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, n.º 3 (1988): 565. http://dx.doi.org/10.2307/2983026.

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Mo, Wen Hui. "Monte Carlo Simulation of Reliability for Gear". Advanced Materials Research 268-270 (julio de 2011): 42–45. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.42.

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Production errors, material properties and applied loads of the gear are stochastic .Considering the influence of these stochastic factors, reliability of gear is studied. The sensitivity analysis of random variable can reduce the number of random variables. Simulating random variables, a lot of samples are generated. Using the Monte Carlo simulation based on the sensitivity analysis, reliabilities of contacting fatigue strength and bending fatigue strength can be obtained. The Monte Carlo simulation approaches the accurate solution gradually with the increase of the number of simulations. The numerical example validates the proposed method.
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Tesis sobre el tema "Stochastic simulation"

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Hellander, Stefan. "Stochastic Simulation of Reaction-Diffusion Processes". Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-198522.

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Numerical simulation methods have become an important tool in the study of chemical reaction networks in living cells. Many systems can, with high accuracy, be modeled by deterministic ordinary differential equations, but other systems require a more detailed level of modeling. Stochastic models at either the mesoscopic level or the microscopic level can be used for cases when molecules are present in low copy numbers. In this thesis we develop efficient and flexible algorithms for simulating systems at the microscopic level. We propose an improvement to the Green's function reaction dynamics algorithm, an efficient microscale method. Furthermore, we describe how to simulate interactions with complex internal structures such as membranes and dynamic fibers. The mesoscopic level is related to the microscopic level through the reaction rates at the respective scale. We derive that relation in both two dimensions and three dimensions and show that the mesoscopic model breaks down if the discretization of space becomes too fine. For a simple model problem we can show exactly when this breakdown occurs. We show how to couple the microscopic scale with the mesoscopic scale in a hybrid method. Using the fact that some systems only display microscale behaviour in parts of the system, we can gain computational time by restricting the fine-grained microscopic simulations to only a part of the system. Finally, we have developed a mesoscopic method that couples simulations in three dimensions with simulations on general embedded lines. The accuracy of the method has been verified by comparing the results with purely microscopic simulations as well as with theoretical predictions.
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Drawert, Brian J. "Spatial Stochastic Simulation of Biochemical Systems". Thesis, University of California, Santa Barbara, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3559784.

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Recent advances in biology have shown that proteins and genes often interact probabilistically. The resulting effects that arise from these stochastic dynamics differ significantly than traditional deterministic formulations, and have biologically significant ramifications. This has led to the development of computational models of the discrete stochastic biochemical pathways found in living organisms. These include spatial stochastic models, where the physical extent of the domain plays an important role; analogous to traditional partial differential equations.

Simulation of spatial stochastic models is a computationally intensive task. We have developed a new algorithm, the Diffusive Finite State Projection (DFSP) method for the efficient and accurate simulation of stochastic spatially inhomogeneous biochemical systems. DFSP makes use of a novel formulation of Finite State Projection (FSP) to simulate diffusion, while reactions are handled by the Stochastic Simulation Algorithm (SSA). Further, we adapt DFSP to three dimensional, unstructured, tetrahedral meshes in inclusion in the mature and widely usable systems biology modeling software URDME, enabling simulation of the complex geometries found in biological systems. Additionally, we extend DFSP with adaptive error control and a highly efficient parallel implementation for the graphics processing units (GPU).

In an effort to understand biological processes that exhibit stochastic dynamics, we have developed a spatial stochastic model of cellular polarization. Specifically we investigate the ability of yeast cells to sense a spatial gradient of mating pheromone and respond by forming a projection in the direction of the mating partner. Our results demonstrates that higher levels of stochastic noise results in increased robustness, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function. This also highlights the importance of spatial stochastic modeling to reproduce experimental observations.

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Homem, de Mello Tito. "Simulation-based methods for stochastic optimization". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.

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Morton-Firth, Carl Jason. "Stochastic simulation of cell signalling pathways". Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625063.

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Cheung, Ricky. "Stochastic based football simulation using data". Thesis, Uppsala universitet, Matematiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359835.

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This thesis is an extension of a football simulator made in a previous project, where we also made different visualizations and simulators based on football data. The goal is to create a football simulator based on a modified Markov chain process, where two teams can be chosen, to simulate entire football matches play-by-play. To validate our model, we compare simulated data with the provided data from Opta. Several adjustments are made to make the simulation as realistic as possible. After conducting a few experiments to compare simulated data with real data before and after adjustments, we conclude that the model may not be adequately accurate to reflect real life matches.
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Du, Manuel. "Stochastic simulation studies for honeybee breeding". Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22295.

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Die Arbeit beschreibt ein stochastisches Simulationsprogramm zur Modellierung von Honigbienenpopulationen unter Zuchtbedingungen. Dieses Programm wurde neu implementiert um unterschiedliche Selektionsstrategien zu evaluieren und zu optimieren. In einer ersten Studie wurde untersucht, inwiefern die Vorhersagen, die das Programm trifft, vom verwendeten genetischen Modell abhängt. Hierbei wurde festgestellt, dass das Finite-Locus-Modell dem Infinitesimalmodell in Langzeitstudien vorzuziehen ist. Eine zweite Studie beleuchtete die Bedeutung der sicheren Anpaarung auf Belegstellen für die Honigbienenzucht. Hier zeigten die Simulationen, dass die Zucht mit Anpaarungskontrolle derjenigen mit freier Paarung von Königinnen deutlich überlegen ist. Schließlich wurde in einer finalen Studie der Frage nachgegangen, wie erfolgreiche Zuchtprogramme bei der Honigbiene langfristig nachhaltig zu gestalten sind. Hierbei sind kurzfristiger genetischer Zugewinn und langfristige Inzuchtvermeidung gegeneinander abzuwägen. Durch umfangreiche Simulationen konnten für verschiedene Ausgangspopulationen Empfehlungen für eine optimale Zuchtintensität auf mütterlicher und väterlicher Seite gefunden werden.
The present work describes a stochastic simulation program for modelling honeybee populations under breeding conditions. The program was newly implemented to investigate and optimize different selection strategies. A first study evaluated in how far the program's predictions depend on the underlying genetic model. It was found that the finite locus model rather than the infinitesimal model should be used for long-term investigations. A second study shed light into the importance of controlled mating for honeybee breeding. It was found that breeding schemes with controlled mating are far superior to free-mating alternatives. Ultimately, a final study examined how successful breeding strategies can be designed so that they are sustainable in the long term. For this, short-term genetic progress has to be weighed against the avoidance of inbreeding in the long run. By extensive simulations, optimal selection intensities on the maternal and paternal paths could be determined for different sets of population parameters.
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Vasan, Arunchandar. "Timestepped stochastic simulation of 802.11 WLANs". College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8533.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Hashemi, Fatemeh Sadat. "Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding". Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76740.

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We consider unconstrained Simulation Optimization (SO) problems, that is, optimization problems where the underlying objective function is unknown but can be estimated at any chosen point by repeatedly executing a Monte Carlo (stochastic) simulation. SO, introduced more than six decades ago through the seminal work of Robbins and Monro (and later by Kiefer and Wolfowitz), has recently generated much attention. Such interest is primarily because of SOs flexibility, allowing the implicit specification of functions within the optimization problem, thereby providing the ability to embed virtually any level of complexity. The result of such versatility has been evident in SOs ready adoption in fields as varied as finance, logistics, healthcare, and telecommunication systems. While SO has become popular over the years, Robbins and Monros original stochastic approximation algorithm and its numerous modern incarnations have seen only mixed success in solving SO problems. The primary reason for this is stochastic approximations explicit reliance on a sequence of algorithmic parameters to guarantee convergence. The theory for choosing such parameters is now well-established, but most such theory focuses on asymptotic performance. Automatically choosing parameters to ensure good finite-time performance has remained vexingly elusive, as evidenced by continuing efforts six decades after the introduction of stochastic approximation! The other popular paradigm to solve SO is what has been called sample-average approximation. Sample-average approximation, more a philosophy than an algorithm to solve SO, attempts to leverage advances in modern nonlinear programming by first constructing a deterministic approximation of the SO problem using a fixed sample size, and then applying an appropriate nonlinear programming method. Sample-average approximation is reasonable as a solution paradigm but again suffers from finite-time inefficiency because of the simplistic manner in which sample sizes are prescribed. It turns out that in many SO contexts, the effort expended to execute the Monte Carlo oracle is the single most computationally expensive operation. Sample-average approximation essentially ignores this issue since, irrespective of where in the search space an incumbent solution resides, prescriptions for sample sizes within sample-average approximation remain the same. Like stochastic approximation, notwithstanding beautiful asymptotic theory, sample-average approximation suffers from the lack of automatic implementations that guarantee good finite-time performance. In this dissertation, we ask: can advances in algorithmic nonlinear programming theory be combined with intelligent sampling to create solution paradigms for SO that perform well in finite-time while exhibiting asymptotically optimal convergence rates? We propose and study a general solution paradigm called Sampling Controlled Stochastic Recursion (SCSR). Two simple ideas are central to SCSR: (i) use any recursion, particularly one that you would use (e.g., Newton and quasi- Newton, fixed-point, trust-region, and derivative-free recursions) if the functions involved in the problem were known through a deterministic oracle; and (ii) estimate objects appearing within the recursions (e.g., function derivatives) using Monte Carlo sampling to the extent required. The idea in (i) exploits advances in algorithmic nonlinear programming. The idea in (ii), with the objective of ensuring good finite-time performance and optimal asymptotic rates, minimizes Monte Carlo sampling by attempting to balance the estimated proximity of an incumbent solution with the sampling error stemming from Monte Carlo. This dissertation studies the theoretical and practical underpinnings of SCSR, leading to implementable algorithms to solve SO. We first analyze SCSR in a general context, identifying various sufficient conditions that ensure convergence of SCSRs iterates to a solution. We then analyze the nature of such convergence. For instance, we demonstrate that in SCSRs which guarantee optimal convergence rates, the speed of the underlying (deterministic) recursion and the extent of Monte Carlo sampling are intimately linked, with faster recursions permitting a wider range of Monte Carlo effort. With the objective of translating such asymptotic results into usable algorithms, we formulate a family of SCSRs called Adaptive SCSR (A-SCSR) that adaptively determines how much to sample as a recursion evolves through the search space. A-SCSRs are dynamic algorithms that identify sample sizes to balance estimated squared bias and variance of an incumbent solution. This makes the sample size (at every iteration of A-SCSR) a stopping time, thereby substantially complicating the analysis of the behavior of A-SCSRs iterates. That A-SCSR works well in practice is not surprising" the use of an appropriate recursion and the careful sample size choice ensures this. Remarkably, however, we show that A-SCSRs are convergent to a solution and exhibit asymptotically optimal convergence rates under conditions that are no less general than what has been established for stochastic approximation algorithms. We end with the application of a certain A-SCSR to a parameter estimation problem arising in the context of brain-computer interfaces (BCI). Specifically, we formulate and reduce the problem of probabilistically deciphering the electroencephalograph (EEG) signals recorded from the brain of a paralyzed patient attempting to perform one of a specified set of tasks. Monte Carlo simulation in this context takes a more general view, as the act of drawing an observation from a large dataset accumulated from the recorded EEG signals. We apply A-SCSR to nine such datasets, showing that in most cases A-SCSR achieves correct prediction rates that are between 5 and 15 percent better than competing algorithms. More importantly, due to the incorporated adaptive sampling strategies, A-SCSR tends to exhibit dramatically better efficiency rates for comparable prediction accuracies.
Ph. D.
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Albertyn, Martin. "Generic simulation modelling of stochastic continuous systems". Thesis, Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05242005-112442.

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Xu, Zhouyi. "Stochastic Modeling and Simulation of Gene Networks". Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/645.

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Recent research in experimental and computational biology has revealed the necessity of using stochastic modeling and simulation to investigate the functionality and dynamics of gene networks. However, there is no sophisticated stochastic modeling techniques and efficient stochastic simulation algorithms (SSA) for analyzing and simulating gene networks. Therefore, the objective of this research is to design highly efficient and accurate SSAs, to develop stochastic models for certain real gene networks and to apply stochastic simulation to investigate such gene networks. To achieve this objective, we developed several novel efficient and accurate SSAs. We also proposed two stochastic models for the circadian system of Drosophila and simulated the dynamics of the system. The K-leap method constrains the total number of reactions in one leap to a properly chosen number thereby improving simulation accuracy. Since the exact SSA is a special case of the K-leap method when K=1, the K-leap method can naturally change from the exact SSA to an approximate leap method during simulation if necessary. The hybrid tau/K-leap and the modified K-leap methods are particularly suitable for simulating gene networks where certain reactant molecular species have a small number of molecules. Although the existing tau-leap methods can significantly speed up stochastic simulation of certain gene networks, the mean of the number of firings of each reaction channel is not equal to the true mean. Therefore, all existing tau-leap methods produce biased results, which limit simulation accuracy and speed. Our unbiased tau-leap methods remove the bias in simulation results that exist in all current leap SSAs and therefore significantly improve simulation accuracy without sacrificing speed. In order to efficiently estimate the probability of rare events in gene networks, we applied the importance sampling technique to the next reaction method (NRM) of the SSA and developed a weighted NRM (wNRM). We further developed a systematic method for selecting the values of importance sampling parameters. Applying our parameter selection method to the wSSA and the wNRM, we get an improved wSSA (iwSSA) and an improved wNRM (iwNRM), which can provide substantial improvement over the wSSA in terms of simulation efficiency and accuracy. We also develop a detailed and a reduced stochastic model for circadian rhythm in Drosophila and employ our SSA to simulate circadian oscillations. Our simulations showed that both models could produce sustained oscillations and that the oscillation is robust to noise in the sense that there is very little variability in oscillation period although there are significant random fluctuations in oscillation peeks. Moreover, although average time delays are essential to simulation of oscillation, random changes in time delays within certain range around fixed average time delay cause little variability in the oscillation period. Our simulation results also showed that both models are robust to parameter variations and that oscillation can be entrained by light/dark circles.
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Libros sobre el tema "Stochastic simulation"

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Stochastic simulation. New York: Wiley, 1987.

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Ripley, Brian D., ed. Stochastic Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1987. http://dx.doi.org/10.1002/9780470316726.

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Shedler, G. S. Regenerative stochastic simulation. Boston: Academic Press, 1993.

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sentralbyrå, Norway Statistisk, ed. Stochastic simulation of KVARTS91. Oslo: Statistisk sentralbyrå, 1993.

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MacKeown, P. K. Stochastic simulation in physics. New York: Springer, 1997.

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Nelson, Barry L. Stochastic modeling: Analysis & simulation. Mineloa, N.Y: Dover Publications, 2002.

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Balakrishnan, N., V. B. Melas y S. Ermakov, eds. Advances in Stochastic Simulation Methods. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1318-5.

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Asmussen, Søren y Peter W. Glynn. Stochastic Simulation: Algorithms and Analysis. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-69033-9.

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Stochastic modeling: Analysis and simulation. New York: McGraw-Hill, 1995.

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Nelson, Barry L. Stochastic modeling: Analysis and simulation. Mineola, N.Y: Dover Publications, 2010.

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Capítulos de libros sobre el tema "Stochastic simulation"

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Drew, John H., Diane L. Evans, Andrew G. Glen y Lawrence M. Leemis. "Stochastic Simulation". En International Series in Operations Research & Management Science, 209–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43323-3_12.

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Olea, Ricardo A. "Stochastic Simulation". En Geostatistics for Engineers and Earth Scientists, 141–62. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5001-3_9.

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Heermann, Dieter W. "Stochastic Simulation". En Encyclopedia of Applied and Computational Mathematics, 1402–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_557.

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Berlinger, Marcel. "Stochastic Simulation". En A Methodology to Model the Statistical Fracture Behavior of Acrylic Glasses for Stochastic Simulation, 93–109. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-34330-9_7.

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Rhinehart, R. Russell y Robert M. Bethea. "Stochastic Simulation". En Applied Engineering Statistics, 189–204. 2a ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003222330-10.

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Romero, Paulo y Martins Maciel. "Stochastic Simulation". En Performance, Reliability, and Availability Evaluation of Computational Systems, Volume I, 705–86. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003306016-14.

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Lantuéjoul, Christian. "Investigating stochastic models". En Geostatistical Simulation, 9–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04808-5_2.

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Grigoriu, Mircea. "Monte Carlo Simulation". En Stochastic Calculus, 287–342. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-0-8176-8228-6_5.

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Haas, Peter J. "Regenerative Simulation". En Stochastic Petri Nets, 189–273. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-21552-2_6.

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Andò, Bruno y Salvatore Graziani. "The Nass Simulation Environment". En Stochastic Resonance, 177–86. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4391-6_6.

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Actas de conferencias sobre el tema "Stochastic simulation"

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Apaydin, Mehmet Serkan, Douglas L. Brutlag, Carlos Guestrin, David Hsu y Jean-Claude Latombe. "Stochastic roadmap simulation". En the sixth annual international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/565196.565199.

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Ghosh, Soumyadip y Henry Lam. "Mirror descent stochastic approximation for computing worst-case stochastic input models". En 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408184.

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Passerat-Palmbach, Jonathan, Jonathan Caux, Yannick Le Pennec, Romain Reuillon, Ivan Junier, François Kepes y David R. C. Hill. "Parallel stepwise stochastic simulation". En the 2013 ACM SIGSIM conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2486092.2486114.

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Dinh, Cong Que, Seiji Nagahara, Gousuke Shiraishi, Yukie Minekawa, Yuya Kamei, Michael Carcasi, Hiroyuki Ide et al. "Calibrated PSCAR stochastic simulation". En Extreme Ultraviolet (EUV) Lithography X, editado por Kenneth A. Goldberg. SPIE, 2019. http://dx.doi.org/10.1117/12.2515183.

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Balbo, G. y G. Chiola. "Stochastic petri net simulation". En the 21st conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/76738.76772.

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Plumlee, Matthew y Henry Lam. "Learning stochastic model discrepancy". En 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822108.

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Shanbhag, Uday V. y Jose H. Blanchet. "Budget-constrained stochastic approximation". En 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408179.

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Dain, Oliver, Matthew Ginsberg, Erin Keenan, John Pyle, Tristan Smith, Andrew Stoneman y Iain Pardoe. "Stochastic Shipyard Simulation with Simyard". En 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.322954.

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Yousefian, Farzad, Angelia Nedic y Uday V. Shanbhag. "A smoothing stochastic quasi-newton method for non-lipschitzian stochastic optimization problems". En 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247960.

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Ankenman, Bruce, Barry L. Nelson y Jeremy Staum. "Stochastic kriging for simulation metamodeling". En 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736089.

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Informes sobre el tema "Stochastic simulation"

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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod y Günther Prokop. Report on design of modules for the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.245.

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As part of the AutoDrive project, OpenPASS is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. The deliverable D4.20 is about the design of the modules for the stochastic traffic simulation. This initially includes an examination of the existing traffic simulations described in chapter 2. Subsequently, the underlying tasks of the driver when crossing an intersection are explained. The main part contains the design of the cognitive structure of the road user (chapter 4.2) and the development of the cognitive behaviour modules (chapter 4.3).
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Bäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod y Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.

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This document is intended to give an overview of the validation of the human subject study, conducted in the driving simulator of the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD), as well of the validation of the stochastic traffic simulation developed in the AutoDrive project by the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD. Furthermore, the evaluation process of a C-AEB (Cooperative-Automatic Emergency Brake) system is demonstrated. The main purpose was to compare the driving behaviour of the study participants and the driving behaviour of the agents in the traffic simulation with real world data. Based on relevant literature, a validation concept was designed and real world data was collected using drones and stationary cameras. By means of qualitative and quantitative analysis it could be shown, that the driving simulator study shows realistic driving behaviour in terms of mean speed. Moreover, the stochastic traffic simulation already reflects reality in terms of mean and maximum speed of the agents. Finally, the performed evaluation proofed the suitability of the developed stochastic simulation for the assessment process. Furthermore, it could be shown, that a C-AEB system improves the traffic safety for the chosen test-scenarios.
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James Glimm y Xiaolin Li. Multiscale Stochastic Simulation and Modeling. Office of Scientific and Technical Information (OSTI), enero de 2006. http://dx.doi.org/10.2172/862194.

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Field, Richard V. ,. Jr. Stochastic models: theory and simulation. Office of Scientific and Technical Information (OSTI), marzo de 2008. http://dx.doi.org/10.2172/932886.

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Ringhand, Madlen, Maximilian Bäumler, Christian Siebke, Marcus Mai y Felix Elrod. Report on validation of the stochastic traffic simulation (Part A). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.242.

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This document is intended to give an overview of the human subject study in a driving simulator that was conducted by the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD) to provide the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD with the necessary input for the validation of a stochastic traffic simulation, especially for the parameterization, consolidation, and validation of driver behaviour models. VPSY planned, conducted, and analysed a driving simulator study. The main purpose of the study was to analyse driving behaviour and gaze data at intersections in urban areas. Based on relevant literature, a simulated driving environment was created, in which a sample of drivers passed a variety of intersections. Considering different driver states, driving tasks, and traffic situations, the collected data provide detailed information about human gaze and driving behaviour when approaching and crossing intersections. The collected data was transferred to LKT for the development of the stochastic traffic simulation.
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Siebke, Christian, Maximilian Bäumler, Madlen Ringhand, Marcus Mai, Felix Elrod y Günther Prokop. Report on integration of the stochastic traffic simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.246.

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As part of the AutoDrive project, the OpenPASS framework is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. This framework was adapted and further developed. The deliverable D5.13 deals with the construction of the stochastic traffic simulation. At this point of the process, the theoretical design aspects of D4.20 are implemented. D5.13 explains the operating principles of the different modules. This includes the foundations, boundary conditions, and mathematical theory of the traffic simulation.
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Glynn, Peter W. Optimization of Stochastic Systems via Simulation. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1989. http://dx.doi.org/10.21236/ada214011.

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Johnson, Ralph. Stochastic Simulation Analysis - 2005 (SSA-05). Fort Belvoir, VA: Defense Technical Information Center, julio de 1997. http://dx.doi.org/10.21236/ada329429.

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Fricks, John y Gustavo Didier. Statistical Inference and Stochastic Simulation for Microrheology. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2013. http://dx.doi.org/10.21236/ada605473.

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Heinisch, H. L. Stochastic annealing simulation of cascades in metals. Office of Scientific and Technical Information (OSTI), abril de 1996. http://dx.doi.org/10.2172/270462.

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