Relevant bibliographies by topics / Monte Carlo Simulation

Academic literature on the topic 'Monte Carlo Simulation'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Monte Carlo Simulation"

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Davidović, Branko, Duško Letić, and Aleksandar Jovanović. "MONTE CARLO SIMULATION IN INTRALOGISTICS." MEST Journal 2, no. 1 (January 15, 2014): 87–93. http://dx.doi.org/10.12709/mest.02.02.01.09.

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Ziegel, Eric R., and C. Mooney. "Monte Carlo Simulation." Technometrics 40, no. 3 (August 1998): 267. http://dx.doi.org/10.2307/1271205.

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Sakota, Daisuke, and Setsuo Takatani. "Photon-cell interactive Monte Carlo simulation." Nippon Laser Igakkaishi 32, no. 4 (2012): 411–20. http://dx.doi.org/10.2530/jslsm.32.411.

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Dou, Mingze. "Principle and Applications of Monte-Carlo Simulation in Forecasting, Algorithm and Health Risk Assessment." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 406–14. http://dx.doi.org/10.54097/jjw5by20.

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Monte Carlo simulation, as a technique to reverse parameters by random sampling in known data, is widely used in many fields such as finance, computer and engineering. While introducing the basic concepts and related principles of Monte Carlo simulation, this paper will focus on three new applications of Monte Carlo simulation in electricity price prediction, algorithm and health risk assessment. The limitations and future development of the Monte Carlo simulation are discussed later. Future research should solve the defects of Monte Carlo simulation with long computing consumption time, lack of evaluation method and strict sampling requirements, and enhance the adaptability of this method by combining the problems worth research in various fields. This paper hopes to provide the reader with the relevant background knowledge of Monte Carlo simulations to facilitate the application of Monte Carlo simulation to complex problems in more domains. Overall, these results shed light on guiding further exploration of applications based on Monte Carlo Simulations.
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Phoa, Wesley. "Conditional Monte Carlo Simulation." Journal of Investing 8, no. 3 (August 31, 1999): 80–88. http://dx.doi.org/10.3905/joi.1999.319371.

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Wang, Yazhen. "Quantum Monte Carlo simulation." Annals of Applied Statistics 5, no. 2A (June 2011): 669–83. http://dx.doi.org/10.1214/10-aoas406.

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SWENDSEN, ROBERT H., BRIAN DIGGS, JIAN-SHENG WANG, SHING-TE LI, CHRISTOPHER GENOVESE, and JOSEPH B. KADANE. "TRANSITION MATRIX MONTE CARLO." International Journal of Modern Physics C 10, no. 08 (December 1999): 1563–69. http://dx.doi.org/10.1142/s0129183199001340.

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Although histogram methods have been extremely effective for analyzing data from Monte Carlo simulations, they do have certain limitations, including the range over which they are valid and the difficulties of combining data from independent simulations. In this paper, we describe a complementary approach to extracting information from Monte Carlo simulations that uses the matrix of transition probabilities. Combining the Transition Matrix with an N-fold way simulation technique produces an extremely flexible and efficient approach to rather general Monte Carlo simulations.
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Mo, Wen Hui. "Monte Carlo Simulation of Reliability for Gear." Advanced Materials Research 268-270 (July 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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Cheng, Minqi, and Jiasheng Guo. "Analysis of the Principle and Two Applications for Monte-Carlo Simulations." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 136–41. http://dx.doi.org/10.54097/3dg18k50.

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As a matter of fact, stochastic process and sampling algorithms are widely used in the state-of-art numerical simulations. In order to evaluate the random effect, the means of Monte-Carlo simulations are widely adopted and used to obtain a convergence or trending results. With this in mind, this essay mainly talks about the two applications of Monte Carlo simulation and the impact of it toward the society and human race. To be specific, firstly, the origin of Monte-Carlo simulation was revealed and its history of development was elaborated. After that, the basic concept of Monte-Carlo analysis was formulated as well as the sampling process of it is done briefly. All those foreshadows were aimed at assisting the readers to obtain a basic idea of this simulating method and be able to comprehend the relatively sophisticated applications, including financial and computer science knowledge. Overall, these results shed light on guiding further exploration of Monte-Carlo simulations.
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JAKUMEIT, JÜRGEN. "COMPUTATIONAL ASPECTS OF THE LOCAL ITERATIVE MONTE CARLO TECHNIQUE." International Journal of Modern Physics C 11, no. 04 (June 2000): 665–73. http://dx.doi.org/10.1142/s0129183100000584.

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Lately, the Local Iterative Monte Carlo technique was introduced for an efficient simulation of effects connected to sparsely populated regions in semiconductor devices like hot electron effects in silicon MOSFETs. This paper focuses on computational aspects of this new Monte Carlo technique, namely the reduction of the computation time by parallel computation and the reuse of drift information. The Local Iterative Monte Carlo technique combines short Monte Carlo particle flight simulations with an iteration process to a complete device simulation. The separation between short Monte Carlo simulations and the iteration process makes a simple parallelization strategy possible. The necessary data transfer is small and can be performed via the Network File System. An almost linear speed up could be achieved. Besides by parallelization, the computational expenses can be significantly reduced, when the results of the short Monte Carlo simulations are memorized in a drift table and used several times. A comparison between a bulk, a one-dimensional and the two-dimensional Local Iterative Monte Carlo simulation reveals that by using the drift information more than once, becomes increasingly efficient with increasing dimension of the simulation.
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Dissertations / Theses on the topic "Monte Carlo Simulation"

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Janzon, Krister. "Monte Carlo Path Simulation and the Multilevel Monte Carlo Method." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151975.

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A standard problem in the field of computational finance is that of pricing derivative securities. This is often accomplished by estimating an expected value of a functional of a stochastic process, defined by a stochastic differential equation (SDE). In such a setting the random sampling algorithm Monte Carlo (MC) is useful, where paths of the process are sampled. However, MC in its standard form (SMC) is inherently slow. Additionally, if the analytical solution to the underlying SDE is not available, a numerical approximation of the process is necessary, adding another layer of computational complexity to the SMC algorithm. Thus, the computational cost of achieving a certain level of accuracy of the estimation using SMC may be relatively high. In this thesis we introduce and review the theory of the SMC method, with and without the need of numerical approximation for path simulation. Two numerical methods for path approximation are introduced: the Euler–Maruyama method and Milstein's method. Moreover, we also introduce and review the theory of a relatively new (2008) MC method – the multilevel Monte Carlo (MLMC) method – which is only applicable when paths are approximated. This method boldly claims that it can – under certain conditions – eradicate the additional complexity stemming from the approximation of paths. With this in mind, we wish to see whether this claim holds when pricing a European call option, where the underlying stock process is modelled by geometric Brownian motion. We also want to compare the performance of MLMC in this scenario to that of SMC, with and without path approximation. Two numerical experiments are performed. The first to determine the optimal implementation of MLMC, a static or adaptive approach. The second to illustrate the difference in performance of adaptive MLMC and SMC – depending on the used numerical method and whether the analytical solution is available. The results show that SMC is inferior to adaptive MLMC if numerical approximation of paths is needed, and that adaptive MLMC seems to meet the complexity of SMC with an analytical solution. However, while the complexity of adaptive MLMC is impressive, it cannot quite compensate for the additional cost of approximating paths, ending up roughly ten times slower than SMC with an analytical solution.
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Lin, Xichen. "Monte Carlo Simulation and Integration." Scholarship @ Claremont, 2018. https://scholarship.claremont.edu/cmc_theses/2009.

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In this paper, we introduce the Tootsie Pop Algorithm and explore its use in different contexts. It can be used to estimate more general problems where a measure is defined, or in the context of statistics application, integration involving high dimensions. The Tootsie Pop Algorithm was introduced by Huber and Schott[2] The general process of Tootsie Pop Algorithm, just like what its name suggests, is a process of peeling down the outer shell, which is the larger enclosing set, to the center, which is the smaller enclosed. We obtain the average number of peels, which gives us an understanding of the ratio between the size of the shell and the size of the center. Each peel is generated by a random draw within the outer shell: if the drawn point is located in the center, we are done, else we update the outer shell such that the drawn point is right on its edge.
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Lee, Ming Ripman, and 李明. "Monte Carlo simulation for confined electrolytes." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31240513.

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Swetnam, Adam D. "Monte Carlo simulation of lattice polymers." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/49196/.

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The phase behaviour of lattice polymers and peptides, under various conditions, is investigated using Monte Carlo simulation. Wang-Landau sampling is used so that, in principle, phase diagrams can be determined from a single simulation. It is demonstrated that the pseudophase diagram for polymer molecules, in several environments, can be plotted when sampling only from the internal degrees of freedom, by determining an appropriate density of states. Several improvements to the simulation methods used are detailed. A new prescription for setting the modification factor in the Wang-Landau algorithm is described, tested and found, for homopolymers, to result in near optimum convergence throughout the simulation. Different methods of selecting moves from the pull move set are detailed, and their relative efficiencies determined. Finally, it is shown that results for a polymer in a slit with one attractive surface can be determined by sampling only from the internal degrees of freedom of a lattice polymer. Adsorption of lattice polymers and peptides is investigated by determining pseudophase diagrams for individual molecules. The phase diagram for a homopolymer molecule, near a surface with a pattern of interaction, is determined, with a pseudophase identified where the polymer is commensurate with the pattern. For an example lattice peptide, the existence of the new pseudophase is found to depend on whether both hydrophobic and polar beads are attracted to the surface. The phase diagram for a ring polymer under applied force, with variable solvent quality, is determined for the first time. The effect, on the phase diagram, of topological knots in the ring polymer is investigated. In addition to eliminating pseudophases where the polymer is flattened into a single layer, it is found that non-trivial knots result in additional pseudophases for tensile force.
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Lee, Ming Ripman. "Monte Carlo simulation for confined electrolytes /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22055009.

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Bryskhe, Henrik. "Optimization of Monte Carlo simulations." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121843.

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This thesis considers several different techniques for optimizing Monte Carlo simulations. The Monte Carlo system used is Penelope but most of the techniques are applicable to other systems. The two mayor techniques are the usage of the graphics card to do geometry calculations, and raytracing. Using graphics card provides a very efficient way to do fast ray and triangle intersections. Raytracing provides an approximation of Monte Carlo simulation but is much faster to perform. A program was also written in order to have a platform for Monte Carlo simulations where the different techniques were implemented and tested. The program also provides an overview of the simulation setup, were the user can easily verify that everything has been setup correctly. The thesis also covers an attempt to rewrite Penelope from FORTAN to C. The new version is significantly faster and can be used on more systems. A distribution package was also added to the new Penelope version. Since Monte Carlo simulations are easily distributed, running this type of simulations on ten computers yields ten times the speedup. Combining the different techniques in the platform provides an easy to use and at the same time efficient way of performing Monte Carlo simulations.

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Voegele, Simon. "Shortfall-Minimierung Theorie und Monte Carlo Simulation /." St. Gallen, 2007. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02922300001/$FILE/02922300001.pdf.

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Jud, Andreas. "Monte-Carlo-Simulation einer Überstruktur auf Lipidmembranen." [S.l. : s.n.], 1998. http://www.diss.fu-berlin.de/1998/18/index.html.

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Yangthaisong, Anucha. "Monte Carlo simulation of silicon-germanium transistors." Thesis, Durham University, 2002. http://etheses.dur.ac.uk/4025/.

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Self-consistent Monte Carlo simulation studies of n-channel Si/SiGe modulation doped field effect transistors (MODFETs) and silicon-on-insulator lateral bipolar junction transistors (SOI- LBJTs) are reported in this thesis. As a preliminary to the device studies Monte Carlo simulations of electron transport in bulk Si strained as if grown on Si(_0.77)Ge(_0.23) and Si(_0.55)Ge(_0.45) substrates have been carried out at 300 K, for field strengths varied from 10(^4) to 2 x 10(^7) Vm(^-1). The calculations indicate an enhancement of the average electron drift velocity when Si is tensilely strained in the growth plane. The enhancement of electron velocity is more marked at low and intermediate electric fields, while at very high fields the velocity saturates at about the same value as unstrained Si. In addition the ensemble Monte Carlo method has been used to study the transient response to a stepped electric field of electrons in strained and unstrained Si. The calculations suggest that significant velocity overshoots occurs in strained material. Simulations of n-channel Si/Si(_1=z)Ge(_z) MODFETs with Ge fractions of 0.23, 0.25, and 0.45 have been performed. Five depletion mode devices with x = 0.23 and 0.25 were studied. The simulations provide information on the microscopic details of carrier behaviour, including carrier velocity, kinetic energy and carrier density, as a function of position in the device. Detailed time-dependent voltage signal analysis has been carried out to test device response and derive the frequency bandwidth. The simulations predict a current gain cut-off frequency of 60 ± 10 GHz for a device with a gate length of 0.07 /nm and a channel length of 0.25 um. Similar studies of depletion and enhancement mode n-channel Si/Sio.55Geo.45 MODFETs with a gate length of 0.18 /im have been carried out. Cut-off frequencies of 60 ±10 GHz and 90± 10 GHz are predicted for the depletion and enhancement mode devices respectively. A Monte Carlo model has also been devised and used to simulate steady state and transient electron and hole transport in SOI-LBJTs. Four devices have been studied and the effects of junction depth and silicon layer thickness have been investigated. The advantage of the silicon-on-insulator technology SOI device is apparent in terms of higher collector current, current gain, and cut-off frequency obtained in comparison with an all-silicon structure. The simulations suggest that the common-emitter current gain of the most promising SOI-LBJT structure considered could have a cut-off frequency approaching 35 ± 5 GHz.
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Mee, Richard A. W. "Monte Carlo simulation of step growth polymerization." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318843.

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Books on the topic "Monte Carlo Simulation"

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Mooney, Christopher. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1997. http://dx.doi.org/10.4135/9781412985116.

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Zhu, Zhen, and Hari Rajagopalan. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908969.

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I, Schueller G., ed. Monte Carlo simulation. Lisse: A.A. Balkema, 2001.

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Hess, Karl, ed. Monte Carlo Device Simulation. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7.

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Thomopoulos, Nick T. Essentials of Monte Carlo Simulation. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6022-0.

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Brandimarte, Paolo. Handbook in Monte Carlo Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593264.

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Gleißner, Werner, and Marco Wolfrum. Risikoaggregation und Monte-Carlo-Simulation. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24274-9.

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Moglestue, C. Monte Carlo simulation of semiconductor devices. London: Chapman & Hall, 1993.

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A, Young Jennifer, and Langley Research Center, eds. Monte Carlo simulation of endlinking oligomers. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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Kroese, Dirk P., Thomas Taimre, Zdravko I. Botev, and Rueven Y. Rubinstein. Simulation and the Monte Carlo Method. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470285312.

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Book chapters on the topic "Monte Carlo Simulation"

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Abadie, L. M., and J. M. Chamorro. "Monte Carlo Simulation." In Lecture Notes in Energy, 113–33. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5592-8_6.

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Shekhar, Shashi, and Hui Xiong. "Monte Carlo Simulation." In Encyclopedia of GIS, 725. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_815.

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Blanchet, Gérard, and Maurice Charbit. "Monte-Carlo Simulation." In Digital Signal and Image Processing Using MATLAB®, 85–106. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119054009.ch3.

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Seydel, Rüdiger U. "Monte-Carlo-Simulation." In Einführung in die numerische Berechnung von Finanzderivaten, 95–130. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-50299-0_3.

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Sokolowski, John A. "Monte Carlo Simulation." In Modeling and Simulation Fundamentals, 131–45. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470590621.ch5.

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Hu, Xiao, Yoshihiko Nonomura, and Masanori Kohno. "Monte Carlo Simulation." In Springer Handbook of Metrology and Testing, 1117–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16641-9_22.

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Grous, Ammar. "Monte Carlo Simulation." In Fracture Mechanics 2, 205–34. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118580028.ch7.

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Verma, Surendra P. "Monte Carlo Simulation." In Road from Geochemistry to Geochemometrics, 379–402. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9278-8_8.

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Nakagiri, Kota. "Monte Carlo Simulation." In Search for the Decay K_L → π^0\nu\bar{\nu} at the J-PARC KOTO Experiment, 65–71. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6422-2_5.

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Hu, Xiao, Yoshihiko Nonomura, and Masanori Kohno. "Monte Carlo Simulation." In Springer Handbook of Materials Measurement Methods, 1057–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-30300-8_22.

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Conference papers on the topic "Monte Carlo Simulation"

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Silver, David, and Gerald Tesauro. "Monte-Carlo simulation balancing." In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553495.

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Graham, G. E. "DZero Monte Carlo simulation." In ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH: VII International Workshop; ACAT 2000. AIP, 2001. http://dx.doi.org/10.1063/1.1405336.

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Rosenbaum, Imry, and Jeremy Staum. "Multilevel Monte Carlo metamodeling." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721446.

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Raychaudhuri, Samik. "Introduction to Monte Carlo simulation." In 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736059.

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Glynn, Peter W. "Monte carlo simulation of diffusions." In 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736113.

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Nakayama, Marvin K., Zachary T. Kaplan, Yajuan Li, Bruno Tuffin, and Pierre L'Ecuyer. "Quantile Estimation Via a Combination of Conditional Monte Carlo and Randomized Quasi-Monte Carlo." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9384031.

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Benson, Rodney, and Darryl Kellner. "Monte Carlo Simulation for Reliability." In 2020 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2020. http://dx.doi.org/10.1109/rams48030.2020.9153600.

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Gao, J., and R. G. Thompson. "Monte Carlo Simulation of Solidification." In Superalloys. TMS, 1997. http://dx.doi.org/10.7449/1997/superalloys_1997_77_86.

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Zhang Jiayou and Chen Qi'an. "Monte Carlo simulation in demography." In 2008 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008). IEEE, 2008. http://dx.doi.org/10.1109/iske.2008.4730958.

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Harrison, Robert L., Carlos Granja, and Claude Leroy. "Introduction to Monte Carlo Simulation." In NUCLEAR PHYSICS METHODS AND ACCELERATORS IN BIOLOGY AND MEDICINE: Fifth International Summer School on Nuclear Physics Methods and Accelerators in Biology and Medicine. AIP, 2010. http://dx.doi.org/10.1063/1.3295638.

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Reports on the topic "Monte Carlo Simulation"

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Glaser, R. Monte Carlo simulation of scenario probability distributions. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/632934.

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Xu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/10134610.

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Douglas, L. J. Monte Carlo Simulation as a Research Management Tool. Office of Scientific and Technical Information (OSTI), June 1986. http://dx.doi.org/10.2172/1129252.

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Aguayo Navarrete, Estanislao, Austin S. Ankney, Timothy J. Berguson, Richard T. Kouzes, John L. Orrell, Meredith D. Troy, and Clinton G. Wiseman. Monte Carlo Simulation Tool Installation and Operation Guide. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1095434.

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Xu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/5494991.

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Boyd, Lain D. Monte Carlo Simulation of Radiation in Hypersonic Flows. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada414031.

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Clegg, Benjamin Wyatt, David H. Collins, Jr., and Aparna V. Huzurbazar. Petri Nets for Adversarial Models using Monte Carlo Simulation. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1473775.

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Rensink, M. E., and T. D. Rognlien. Progress on coupling UEDGE and Monte-Carlo simulation codes. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/380308.

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Martin, William R., James Paul Holloway, Kaushik Banerjee, Jesse Cheatham, and Jeremy Conlin. Global Monte Carlo Simulation with High Order Polynomial Expansions. Office of Scientific and Technical Information (OSTI), December 2007. http://dx.doi.org/10.2172/920974.

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Holliday, Mary R. Methodology of an Event-Driven Monte Carlo Missile Simulation. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada601300.

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