Letteratura scientifica selezionata sul tema "Monte Carlo method"

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Articoli di riviste sul tema "Monte Carlo method"

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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
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Makarova, K. V., A. G. Makarov, M. A. Padalko, V. S. Strongin, and K. V. Nefedev. "Multispin Monte Carlo Method." Dal'nevostochnyi Matematicheskii Zhurnal 20, no. 2 (2020): 212–20. http://dx.doi.org/10.47910/femj202020.

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The article offers a Monte Carlo cluster method for numerically calculating a statistical sample of the state space of vector models. The statistical equivalence of subsystems in the Ising model and quasi-Markov random walks can be used to increase the efficiency of the algorithm for calculating thermodynamic means. The cluster multispin approach extends the computational capabilities of the Metropolis algorithm and allows one to find configurations of the ground and low-energy states.
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Rajabalinejad, M. "Bayesian Monte Carlo method." Reliability Engineering & System Safety 95, no. 10 (2010): 1050–60. http://dx.doi.org/10.1016/j.ress.2010.04.014.

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The Lam, Nguyen. "QUANTUM DIFFUSION MONTE CARLO METHOD FOR LOW-DIMENTIONAL SYSTEMS." Journal of Science, Natural Science 60, no. 7 (2015): 81–87. http://dx.doi.org/10.18173/2354-1059.2015-0036.

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Siyamah, Imroatus, Endah RM Putri, and Chairul Imron. "Cat bond valuation using Monte Carlo and quasi Monte Carlo method." Journal of Physics: Conference Series 1821, no. 1 (2021): 012053. http://dx.doi.org/10.1088/1742-6596/1821/1/012053.

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Rashki, Mohsen. "The soft Monte Carlo method." Applied Mathematical Modelling 94 (June 2021): 558–75. http://dx.doi.org/10.1016/j.apm.2021.01.022.

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Aboughantous, Charles H. "A Contributorn Monte Carlo Method." Nuclear Science and Engineering 118, no. 3 (1994): 160–77. http://dx.doi.org/10.13182/nse94-a19382.

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Bruce, A. D., A. N. Jackson, G. J. Ackland, and N. B. Wilding. "Lattice-switch Monte Carlo method." Physical Review E 61, no. 1 (2000): 906–19. http://dx.doi.org/10.1103/physreve.61.906.

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Gubernatis, Jim, and Naomichi Hatano. "The multicanonical Monte Carlo method." Computing in Science & Engineering 2, no. 2 (2000): 95–102. http://dx.doi.org/10.1109/mcise.2000.5427643.

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Janke, Wolfhard, and Tilman Sauer. "Multicanonical multigrid Monte Carlo method." Physical Review E 49, no. 4 (1994): 3475–79. http://dx.doi.org/10.1103/physreve.49.3475.

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Tesi sul tema "Monte Carlo method"

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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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Lacasse, Martin Daniel. "New dynamical Monte Carlo renormalization group method." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=60062.

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The kinetics of a phase transition has been studied by using a new dynamical Monte Carlo renormalization group method. Using a majority rule block-spin transformation in both space and contiguous times, we numerically renormalized the evolving configurations during the phase separation of a kinetic Ising model with spin-flip dynamics. We find that, in the scaling regime, the average domain size R(t) grows in time consistently with the $R sim t sp{1/2}$ Allen-Cahn antiphase boundary motion theory, although some correcting factors may exist. The same procedure has also been applied to the corres
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Zhang, Yichuan. "Scalable geometric Markov chain Monte Carlo." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20978.

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Markov chain Monte Carlo (MCMC) is one of the most popular statistical inference methods in machine learning. Recent work shows that a significant improvement of the statistical efficiency of MCMC on complex distributions can be achieved by exploiting geometric properties of the target distribution. This is known as geometric MCMC. However, many such methods, like Riemannian manifold Hamiltonian Monte Carlo (RMHMC), are computationally challenging to scale up to high dimensional distributions. The primary goal of this thesis is to develop novel geometric MCMC methods applicable to large-scale
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Veld, Pieter Jacob in 't. "Monte Carlo studies of liquid structure /." Digital version:, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p9992826.

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Hazelton, Martin Luke. "Method of density estimation with application to Monte Carlo methods." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334850.

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Lefebvre, Geneviève 1978. "Practical issues in modern Monte Carlo integration." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103209.

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Computing marginal likelihoods to perform Bayesian model selection is a challenging task, particularly when the models considered involve a large number of parameters. In this thesis, we propose the use of an adaptive quadrature algorithm to automate the selection of the grid in path sampling, an integration technique recognized as one of the most powerful Monte Carlo integration statistical methods for marginal likelihood estimation. We begin by examining the impact of two tuning parameters of path sampling, the choice of the importance density and the specification of the grid, which are bot
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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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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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Yam, Chiu Yu. "Quasi-Monte Carlo methods for bootstrap." HKBU Institutional Repository, 2000. http://repository.hkbu.edu.hk/etd_ra/272.

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Wong, Ping-yung. "Molecular clusters on surfaces : a Monte Carlo study /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20566694.

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Libri sul tema "Monte Carlo method"

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

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Kalos, Malvin H. Monte Carlo methods. J. Wiley & Sons, 1986.

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Dunn, William L. Exploring Monte Carlo methods. Elsevier/Academic Press, 2012.

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1957-, Madras Neal Noah, Fields Institute for Research in Mathematical Sciences., and Workshop on Monte Carlo Methods (1998 : Toronto, Ont.), eds. Monte Carlo methods. American Mathematical Society, 2000.

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

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Fox, Bennett L. Strategies for quasi-Monte Carlo. Kluwer Academic, 1999.

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Pierre, L' Ecuyer, and Owen Art B, eds. Monte Carlo and quasi-Monte Carlo methods 2008. Springer, 2009.

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Casella, George, and Christian P. Robert. Monte Carlo Statistical Methods. 2nd ed. Springer, 2004.

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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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Rubinstein, Reuven Y., and Dirk P. Kroese. Simulation and the Monte Carlo Method. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118631980.

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Capitoli di libri sul tema "Monte Carlo method"

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Liou, William W. "Monte Carlo Method." In Encyclopedia of Microfluidics and Nanofluidics. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-5491-5_1059.

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Liou, William W. "Monte Carlo Method." In Encyclopedia of Microfluidics and Nanofluidics. Springer US, 2013. http://dx.doi.org/10.1007/978-3-642-27758-0_1059-3.

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Weik, Martin H. "Monte Carlo method." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_11803.

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Mosegaard, Klaus. "Monte Carlo Method." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_431.

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Mosegaard, Klaus. "Monte Carlo Method." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_431-2.

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Mosegaard, Klaus. "Monte Carlo Method." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_431-1.

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Choe, Geon Ho. "The Monte Carlo Method for Option Pricing Monte Carlo method." In Universitext. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25589-7_28.

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Buckley, James J., and Leonard J. Jowers. "Fuzzy Monte Carlo Method." In Monte Carlo Methods in Fuzzy Optimization. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76290-4_6.

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Rollett, Anthony D., and Priya Manohar. "The Monte Carlo Method." In Continuum Scale Simulation of Engineering Materials. Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527603786.ch4.

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Tildesley, D. J. "The Monte Carlo Method." In Computer Simulation in Chemical Physics. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1679-4_1.

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Atti di convegni sul tema "Monte Carlo method"

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Wilding, Nigel B. "Phase Switch Monte Carlo." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632147.

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Frenkel, D. "Biased Monte Carlo Methods." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632121.

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Bilgin, Muhammed, and Tolga Ensari. "Robot localization with Monte Carlo method." In 2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT). IEEE, 2017. http://dx.doi.org/10.1109/ebbt.2017.7956755.

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Ling*, Yue, Huazhong Wang, and Shaoyong Liu. "Monte Carlo background velocity inversion method." In Beijing 2014 International Geophysical Conference & Exposition, Beijing, China, 21-24 April 2014. Society of Exploration Geophysicists and Chinese Petroleum Society, 2014. http://dx.doi.org/10.1190/igcbeijing2014-188.

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Papp, Zsolt, Janos Kornis, and Balazs Gombkoto. "Monte Carlo method in digital holography." In Speckle Metrology 2003. SPIE, 2003. http://dx.doi.org/10.1117/12.516573.

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Gonzalez-Jorge, H., J. L. Valencia, V. Alvarez, F. Rodriguez, and F. J. Yebra. "Monte-Carlo method in AFM calibration." In 2009 Spanish Conference on Electron Devices (CDE). IEEE, 2009. http://dx.doi.org/10.1109/sced.2009.4800526.

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Stoffova, Veronika, and R. Horváth. "MONTE CARLO METHOD IN EDUCATIONAL PRACTICE." In 13th annual International Conference of Education, Research and Innovation. IATED, 2020. http://dx.doi.org/10.21125/iceri.2020.1532.

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Zhu, Juan, Shuai Wang, Da-wei Wang, Yan-ying Liu, and Yan-jie Wang. "Monte Carlo Tracking Method with Threshold Constraint." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5301685.

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Chen, Nanguang. "Controlled Monte Carlo Method for Reflection Geometry." In Biomedical Topical Meeting. OSA, 2006. http://dx.doi.org/10.1364/bio.2006.me9.

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DIMOV, IVAN, and ANETA KARAIVANOVA. "A POWER METHOD WITH MONTE CARLO ITERATIONS." In Proceedings of the Fourth International Conference. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814291071_0022.

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Rapporti di organizzazioni sul tema "Monte Carlo method"

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Hill, James Lloyd. Introduction to the Monte Carlo Method. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1634920.

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Blomquist, R. N., and E. M. Gelbard. Alternative implementations of the Monte Carlo power method. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/793906.

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Svatos, M. The macro response Monte Carlo method for electron transport. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/3847.

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Fishman, George S. Sensitivity Analysis Using the Monte Carlo Acceptance-Rejection Method. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada201261.

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Carlin, Bradley P., and Alan E. Gelfand. An Iterative Monte Carlo Method for Nonconjugate Bayesian Analysis. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada255991.

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Taro Ueki. A Multivariate Time Series Method for Monte Carlo Reactor Analysis. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/935876.

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Richie, David A., James A. Ross, Song J. Park, and Dale R. Shires. A Monte Carlo Method for Multi-Objective Correlated Geometric Optimization. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603830.

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Acton, Scott T., and Bing Li. A Sequential Monte Carlo Method for Real-time Tracking of Multiple Targets. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532576.

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Boyd, Iain D. A Threshold Line Dissociation Model for the Direct Simulation Monte Carlo Method,. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada324950.

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Politis, Dimitris N., Raffaella Giacomini, and Halbert White. A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators. Cemmap, 2012. http://dx.doi.org/10.1920/wp.cem.2012.1112.

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