Relevant bibliographies by topics / Monte Carlo

Academic literature on the topic 'Monte Carlo'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 September 2024

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Journal articles on the topic "Monte Carlo"

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Caflisch, Russel E. "Monte Carlo and quasi-Monte Carlo methods." Acta Numerica 7 (January 1998): 1–49. http://dx.doi.org/10.1017/s0962492900002804.

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Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((logN)kN−1). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described.
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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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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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Xiaopeng Xu, Xiaopeng Xu, Chuancai Liu Xiaopeng Xu, Hongji Yang Chuancai Liu, and Xiaochun Zhang Hongji Yang. "A Multi-Trajectory Monte Carlo Sampler." 網際網路技術學刊 23, no. 5 (September 2022): 1117–28. http://dx.doi.org/10.53106/160792642022092305020.

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<p>Markov Chain Monte Carlo techniques based on Hamiltonian dynamics can sample the first or last principal components of multivariate probability models using simulated trajectories. However, when components&rsquo; scales span orders of magnitude, these approaches may be unable of accessing all components adequately. While it is possible to reconcile the first and last components by alternating between two different types of trajectories, the sampling of intermediate components may be imprecise. In this paper, a function generalizing the kinetic energies of Hamiltonian Monte Carlo and Riemannian Manifold Hamiltonian Monte Carlo is proposed, and it is found that the methods based on a specific form of the function can more accurately sample normal distributions. Additionally, the multi-particle algorithm&rsquo;s reasoning is given after a review of some statistical ideas.</p> <p>&nbsp;</p>
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Alrammal, Muath, and Munir Naveed. "Monte-Carlo Based Reinforcement Learning (MCRL)." International Journal of Machine Learning and Computing 10, no. 2 (February 2020): 227–32. http://dx.doi.org/10.18178/ijmlc.2020.10.2.924.

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Todorov, Venelin. "COMPUTING HIGH DIMENSIONAL INTEGRALS WITH MONTE CARLO METHODS." Journal Scientific and Applied Research 10, no. 1 (October 10, 2016): 11–16. http://dx.doi.org/10.46687/jsar.v10i1.200.

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High dimensional integrals are usually solved with Monte Carlo algorithms and quasi Monte Carlo algorithms. We are doing numerical testing which compare low discrepancy and Monte Carlo algorithms. It is well known that Sobol algorithm has some advantageous over the other low discrepancy sequences, that’s why we use this algorithm for our numerical example. The obtained relative error confirms this superiority of the presented Monte Carlo and quasi Monte Carlo algorithms even when small number of sample points are used. It is very interesting that the presented high dimensional integral gives very low relative error even for computational time less than one second which shows the great importance of the developed algorithms.
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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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Tesauro, Gerald. "MONTE-CARLO BACKGAMMON." ICGA Journal 30, no. 3 (September 1, 2007): 183. http://dx.doi.org/10.3233/icg-2007-30317.

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Hartmann, Dap. "Monte-Carlo Galore!" ICGA Journal 32, no. 1 (March 1, 2009): 41–42. http://dx.doi.org/10.3233/icg-2009-32106.

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Bardenet, Rémi. "Monte Carlo methods." EPJ Web of Conferences 55 (2013): 02002. http://dx.doi.org/10.1051/epjconf/20135502002.

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Dissertations / Theses on the topic "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 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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Jun, Seong-Hwan. "Entangled Monte Carlo." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44953.

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A recurrent problem in statistics is that of computing an expectation involving intractable integration. In particular, this problem arises in Bayesian statistics when computing an expectation with respect to a posterior distribution known only up to a normalizing constant. A common solution is to use Monte Carlo simulation to estimate the target expectation. Two of the most commonly adopted simulation methods are Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods. However, these methods fail to scale up with the size of the inference problem. For MCMC, the problem takes the form of simulations that must be ran for a long time in order to obtain an accurate inference. For SMC, one may not be able to store enough particles to exhaustively explore the state space. We propose a novel scalable parallelization of Monte Carlo simulation, Entangled Monte Carlo simulation, that can scale up with the size of the inference problem. Instead of transmitting particles over the network, our proposed algorithm reconstructs the particles from the particle genealogy using the notion of stochastic maps borrowed from perfect simulation literature. We propose bounds on the expected time for particles to coalesce based on the coalescent model. Our empirical results also demonstrate the efficacy of our method on datasets from the field of phylogenetics.
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Dickinson, Andrew Samuel. "On the analysis of Monte Carlo and quasi-Monte Carlo methods." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409715.

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Göncü, Ahmet. "Monte Carlo and quasi-Monte Carlo methods in pricing financial derivatives." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-06232009-140439/.

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Thesis (Ph. D.)--Florida State University, 2009.
Advisor: Giray Ökten, Florida State University, College of Arts and Sciences, Dept. of Mathematics. Title and description from dissertation home page (viewed on Oct. 5, 2009). Document formatted into pages; contains x, 105 pages. Includes bibliographical references.
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Berezovska, Ganna [Verfasser], and Alexander [Akademischer Betreuer] Blumen. "Monte Carlo study of semiflexible polymers = Monte Carlo Studie von semiflexiblen Polymeren." Freiburg : Universität, 2011. http://d-nb.info/1125885467/34.

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Drumond, Lorenzo. "Il Metodo Monte Carlo." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20698/.

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La tesi introduce le tecniche di approssimazione numerica note come Metodo Monte Carlo. Dopo una breve presentazione dell'apparato teorico che giustifica il funzionamento di questo metodo, si procede con uno studio sulla stima dell'errore, focalizzando l'interesse sulla dimostrazione del Teorema di Berry-Esseen. La tesi continua affrontando il tema della generazione di numeri pseudocasuali, fondamentali nel Metodo Monte Carlo, per poi finire con tre esempi di applicazione.
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Chin, Mary Pik Wai. "Monte Carlo portal dosimetry." Thesis, Cardiff University, 2005. http://orca.cf.ac.uk/54085/.

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This project developed a solution for verifying external photon beam radiotherapy. The solution is based on a calibration chain for deriving portal dose maps from acquired portal images, and a calculation framework for predicting portal dose maps. Quantitative comparison between acquired and predicted portal dose maps accomplishes both geometric (patient positioning with respect to the beam) and dosimetric (2D fluence distribution of the beam) verifications. A disagreement would indicate that beam delivery had not been according to plan. The solution addresses the clinical need for verifying radiotherapy both pre-treatment (without patient in the beam) and on-treatment (with patient in the beam). Medical linear accelerators mounted with electronic portal imaging devices (EPIDs) were used to acquire portal images. Two types of EPIDs were investigated: the amorphous silicon (a-Si) and the scanning liquid ion chamber (SLIC). The EGSnrc family of Monte Carlo codes were used to predict portal dose maps by computer simulation of radiation transport in the beam-phantom-EPID configuration. Monte Carlo simulations have been implemented on several levels of High Throughput Computing (HTC), including the Grid, to reduce computation time. The solution has been tested across the entire clinical range of gantry angle, beam size (5 cm x 5 cm to 20 cm x 20 cm), beam-patient and patient-EPID separations (4 cm to 38 cm). In these tests of known beam-phantom-EPID configurations, agreement between acquired and predicted portal dose profiles was consistently within 2% of the central axis value. This Monte Carlo portal dosimetry solution therefore achieved combined versatility, accuracy and speed not readily achievable by other techniques.
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McNeil-Watson, Graham. "Phase switch Monte Carlo." Thesis, University of Bath, 2007. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486842.

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Computational studies of phase behaviour have always proved difficult, since phase . transitions are inherently slow processes compared to accessible simulation timescales. Despite valiant efforts by researchers there remains a dearth of efficient, robust and scalable methods for determining phase equilibria,· especially in the case of fluid-crystalline solid transitions. This thesis is about such phase coexistence problems, the existing solutions, and more advanced methods that have only recently come into their own. ... Extended sampling methods are examined in detail, and applied to a testbed system, the critical point Lennard-Jones fluid, leading to an estimate of the system free energy in the thermodynamic limit. Then a comparatively new technique, phase switch Monte Carlo (Phys. Rev. Lett. 85, 5138) is applied initially to the venerable hard sphere system. The method overcomes many of the shortcomings present in other works by directly connecting the coexisting phases in a single simulation, and doing so without creating an artificial inter-phase route but rather affecting a direct 'phase leap' from one phase to the other. Finally, phase switch is generalised to soft potentials and applied to the Lennard-Jones freezing transition, resulting in an extensive mapping of the phase boundary for a variety of system sizes (J. Chern. Phys 124, 064504).
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Bakra, Eleni. "Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/1247/.

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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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Books on the topic "Monte Carlo"

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Poncela, Enrique Jardiel. Carlo Monte en Monte Carlo. Madrid: Teatro Español, 1996.

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Fishman, George S. Monte Carlo. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2553-7.

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Medbourne, Paul. Monte Carlo. 2nd ed. Peterborough: Thomas Cook, 2008.

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Lemieux, Christiane. Monte carlo and quasi-monte carlo sampling. New York: Springer, 2009.

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Keller, Alexander, ed. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98319-2.

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Cools, Ronald, and Dirk Nuyens, eds. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33507-0.

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Owen, Art B., and Peter W. Glynn, eds. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91436-7.

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Tuffin, Bruno, and Pierre L'Ecuyer, eds. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43465-6.

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Hinrichs, Aicke, Peter Kritzer, and Friedrich Pillichshammer, eds. Monte Carlo and Quasi-Monte Carlo Methods. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59762-6.

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Keller, Alexander, Stefan Heinrich, and Harald Niederreiter, eds. Monte Carlo and Quasi-Monte Carlo Methods 2006. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-74496-2.

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Book chapters on the topic "Monte Carlo"

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Owen, Art B. "Monte Carlo, Quasi-Monte Carlo, and Randomized Quasi-Monte Carlo." In Monte-Carlo and Quasi-Monte Carlo Methods 1998, 86–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59657-5_5.

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Brémaud, Pierre. "Monte Carlo." In Discrete Probability Models and Methods, 457–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43476-6_19.

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Berlinsky, A. J., and A. B. Harris. "Monte Carlo." In Statistical Mechanics, 477–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28187-8_18.

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Hooten, Mevin B., and Trevor J. Hefley. "Monte Carlo." In Bringing Bayesian Models to Life, 17–23. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653-3.

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Brockhaus, Oliver. "Monte Carlo." In Equity Derivatives and Hybrids, 233–52. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137349491_17.

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Marks, Robert E. "Monte Carlo." In The Palgrave Encyclopedia of Strategic Management, 1058–62. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-137-00772-8_709.

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Marks, Robert E. "Monte Carlo." In The Palgrave Encyclopedia of Strategic Management, 1–4. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-349-94848-2_709-1.

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Fishman, George S. "Introduction." In Monte Carlo, 1–12. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2553-7_1.

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Fishman, George S. "Estimating Volume and Count." In Monte Carlo, 13–144. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2553-7_2.

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Fishman, George S. "Generating Samples." In Monte Carlo, 145–254. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2553-7_3.

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Conference papers on the topic "Monte Carlo"

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Chong, Jike, Ekaterina Gonina, and Kurt Keutzer. "Monte Carlo methods." In the 2010 Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1953611.1953626.

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FRENKEL, DAAN. "Monte Carlo simulations." In Proceedings of the International School of Physics. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789812839664_0004.

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KALOS, M. H. "MONTE CARLO METHODS." In Edward Teller Centennial Symposium - Modern Physics and the Scientific Legacy of Edward Teller. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789812838001_0008.

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Sfikas, Konstantinos, Antonios Liapis, and Georgios N. Yannakakis. "Monte Carlo elites." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3459321.

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Maasar, M. A., N. A. M. Nordin, M. Anthonyrajah, W. M. W. Zainodin, and A. M. Yamin. "Monte Carlo & Quasi-Monte Carlo approach in option pricing." In 2012 IEEE Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, 2012. http://dx.doi.org/10.1109/shuser.2012.6268822.

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FAURE, HENRI. "MONTE-CARLO AND QUASI-MONTE-CARLO METHODS FOR NUMERICAL INTEGRATION." In Present and Future. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799890_0001.

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Moser, Dallas. "Neutron Transport Modeling using Monte Carlo Codes." In Neutron Transport Modeling using Monte Carlo Codes. US DOE, 2022. http://dx.doi.org/10.2172/1909215.

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"Monte-Carlo Image Retargeting." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004744404020408.

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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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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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Reports on the topic "Monte Carlo"

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Vogel, Thomas. Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148317.

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Brown, F. B., and T. M. Sutton. Monte Carlo fundamentals. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/270327.

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Trahan, Travis. Monte Carlo Transport. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1874903.

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Wollaber, Allan Benton. Fundamentals of Monte Carlo. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1258353.

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Lewis, Elmir E. Monte Carlo Reliability Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada210052.

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Lewis, E. E., and Zhuguo Tu. Monte Carlo Reliability Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 1986. http://dx.doi.org/10.21236/ada181406.

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Gubernatis, James E. A Monte Carlo Sampler. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1052793.

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Lewis, E. E. Monte Carlo Reliability Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada162379.

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Cullen, D. Monte Carlo Statistical Convergence. Office of Scientific and Technical Information (OSTI), April 2023. http://dx.doi.org/10.2172/1972034.

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Picard, Richard Roy, Anthony J. Zukaitis, and Robert Authur Forster, III. Evaluating Equivalent Monte Carlo Calculations. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1475321.

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