Relevant bibliographies by topics / Simulation of Monte Carlo

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Simulation of Monte Carlo'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 June 2025

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Journal articles on the topic "Simulation of Monte Carlo"

1

Davidović, Branko, Duško Letić, and Aleksandar Jovanović. "MONTE CARLO SIMULATION IN INTRALOGISTICS." MEST Journal 2, no. 1 (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 (1998): 267. http://dx.doi.org/10.2307/1271205.

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Chen, Jiming. "Monte Carlo Simulations in Complex Systems: Challenges and New Approaches." Theoretical and Natural Science 86, no. 1 (2025): 114–19. https://doi.org/10.54254/2753-8818/2025.20172.

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Monte Carlo simulations are crucial for examining the Ising model, especially when it's tough to find analytical solutions. However, traditional Monte Carlo methods, like the Metropolis algorithm, encounter significant hurdles, such as slowing down near phase transitions and issues related to finite sizes. This paper looks into both the advantages and limitations of these traditional Monte Carlo techniques. It also covers recent developments like Tensor Network Monte Carlo and Quantum Monte Carlo methods, which have shown promise in overcoming these challenges. Furthermore, the paper explores
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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
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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 (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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Phoa, Wesley. "Conditional Monte Carlo Simulation." Journal of Investing 8, no. 3 (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 (2011): 669–83. http://dx.doi.org/10.1214/10-aoas406.

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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
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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
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Dissertations / Theses on the topic "Simulation of Monte Carlo"

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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
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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
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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 algori
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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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<p>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
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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
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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 "Simulation of Monte Carlo"

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Mooney, Christopher. Monte Carlo Simulation. SAGE Publications, Inc., 1997. http://dx.doi.org/10.4135/9781412985116.

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Zhu, Zhen, and Hari Rajagopalan. Monte Carlo Simulation. SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908969.

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

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Hess, Karl, ed. Monte Carlo Device Simulation. 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. 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. 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. 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. Chapman & Hall, 1993.

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A, Young Jennifer, and Langley Research Center, eds. Monte Carlo simulation of endlinking oligomers. 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. John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470285312.

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Book chapters on the topic "Simulation of Monte Carlo"

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Abadie, L. M., and J. M. Chamorro. "Monte Carlo Simulation." In Lecture Notes in Energy. 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. 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®. 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. 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. 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. 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. 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. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9278-8_8.

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Nakagiri, Kota. "Monte Carlo Simulation". У Search for the Decay K_L → π^0\nu\bar{\nu} at the J-PARC KOTO Experiment. 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. 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 "Simulation of Monte Carlo"

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Engelhardt, G. R., and D. D. Macdonald. "Monte Carlo Simulation of Localized Corrosion." In CORROSION 2008. NACE International, 2008. https://doi.org/10.5006/c2008-08270.

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Abstract A review is presented of deterministic Monte Carlo simulation (DMCS) method for describing the development of localized corrosion damage. The main idea of this method is to keep track of each stable pit (or crack) that nucleates, propagates, and repassivates on the metal surface. DMCS is “deterministic”, because the component models are explicitly constrained by the relevant natural laws and because the corrosion evolutionary path is specified. A great advantage of this method lies in the fact that this approach allows us to take into account the interactions between particular indivi
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Silver, David, and Gerald Tesauro. "Monte-Carlo simulation balancing." In the 26th Annual International Conference. 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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Reports on the topic "Simulation of Monte Carlo"

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Glaser, R. Monte Carlo simulation of scenario probability distributions. Office of Scientific and Technical Information (OSTI), 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), 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), 1986. http://dx.doi.org/10.2172/1129252.

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Aguayo Navarrete, Estanislao, Austin S. Ankney, Timothy J. Berguson, et al. Monte Carlo Simulation Tool Installation and Operation Guide. Office of Scientific and Technical Information (OSTI), 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), 1992. http://dx.doi.org/10.2172/5494991.

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

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Blue, James L., Isabel Beichl, and Francis Sullivan. Faster BKL Monte Carlo simulations. National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5489.

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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), 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), 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), 2007. http://dx.doi.org/10.2172/920974.

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