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Journal articles on the topic 'Mètode de Monte Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

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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2

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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3

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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4

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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5

Van Houcke, Kris, Evgeny Kozik, N. Prokof’ev, and B. Svistunov. "Diagrammatic Monte Carlo." Physics Procedia 6 (2010): 95–105. http://dx.doi.org/10.1016/j.phpro.2010.09.034.

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6

Jirari, H., H. Kröger, X. Q. Luo, and K. J. M. Moriarty. "Monte Carlo Hamiltonian." Physics Letters A 258, no. 1 (July 1999): 6–14. http://dx.doi.org/10.1016/s0375-9601(99)00304-7.

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7

Mitas, Lubos. "Quantum Monte Carlo." Current Opinion in Solid State and Materials Science 2, no. 6 (December 1997): 696–700. http://dx.doi.org/10.1016/s1359-0286(97)80012-5.

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8

Stodółkiewicz, J. S. "Monte-Carlo Calculations." Symposium - International Astronomical Union 113 (1985): 361–72. http://dx.doi.org/10.1017/s0074180900147606.

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The evolution of a nonisolated globular cluster is presented. The binaries (both, tidally captured and formed in three-body interactions), outflow of mass from stellar envelopes and shocks are considered as sources of energy in the cluster.

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9

Ferrante, D. D., J. Doll, G. S. Guralnik, and D. Sabo. "Mollified Monte Carlo." Nuclear Physics B - Proceedings Supplements 119 (May 2003): 965–67. http://dx.doi.org/10.1016/s0920-5632(03)01732-8.

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10

Sullivan, Francis. "Learning Monte Carlo." Computing in Science & Engineering 19, no. 1 (January 2017): 86–87. http://dx.doi.org/10.1109/mcse.2017.11.

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11

Cappé, O., A. Guillin, J. M. Marin, and C. P. Robert. "Population Monte Carlo." Journal of Computational and Graphical Statistics 13, no. 4 (December 2004): 907–29. http://dx.doi.org/10.1198/106186004x12803.

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12

Dai, Hongsheng, Murray Pollock, and Gareth Roberts. "Monte Carlo fusion." Journal of Applied Probability 56, no. 01 (March 2019): 174–91. http://dx.doi.org/10.1017/jpr.2019.12.

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AbstractIn this paper we propose a new theory and methodology to tackle the problem of unifying Monte Carlo samples from distributed densities into a single Monte Carlo draw from the target density. This surprisingly challenging problem arises in many settings (for instance, expert elicitation, multiview learning, distributed ‘big data’ problems, etc.), but to date the framework and methodology proposed in this paper (Monte Carlo fusion) is the first general approach which avoids any form of approximation error in obtaining the unified inference. In this paper we focus on the key theoretical underpinnings of this new methodology, and simple (direct) Monte Carlo interpretations of the theory. There is considerable scope to tailor the theory introduced in this paper to particular application settings (such as the big data setting), construct efficient parallelised schemes, understand the approximation and computational efficiencies of other such unification paradigms, and explore new theoretical and methodological directions.

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13

Pederiva, Francesco, and M. H. Kalos. "Fermion Monte Carlo." Computer Physics Communications 121-122 (September 1999): 440–45. http://dx.doi.org/10.1016/s0010-4655(99)00378-1.

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14

Jirari, H. "Monte Carlo Hamiltonian." Nuclear Physics B - Proceedings Supplements 83-84, no. 1-3 (March 2000): 953–55. http://dx.doi.org/10.1016/s0920-5632(00)00372-8.

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15

Jirari, H., H. Kröger, Chun-Qing Huang, Jun-Qin Jiang, X. Q. Luo, and K. J. M. Moriarty. "Monte Carlo Hamiltonian." Nuclear Physics B - Proceedings Supplements 83-84 (April 2000): 953–55. http://dx.doi.org/10.1016/s0920-5632(00)91855-3.

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16

Giersz, Mirek. "Monte-Carlo Simulations." Symposium - International Astronomical Union 174 (1996): 101–10. http://dx.doi.org/10.1017/s0074180900001431.

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The revision of the Stodółkiewicz's Monte-Carlo code is presented. It treats each superstar as a single star and follows the evolution and motion of all individual stellar objects. The first calculations, for equalmass N-body systems with three-body energy generation accordingly to Spitzer's formulae, show good agreement with the direct N-body calculations for N = 2000 and 10000 particles.

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17

Jiménez, Javier. "Monte Carlo science." Journal of Turbulence 21, no. 9-10 (March 19, 2020): 544–66. http://dx.doi.org/10.1080/14685248.2020.1742918.

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18

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

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19

Kroese, Dirk P., and Reuven Y. Rubinstein. "Monte Carlo methods." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 1 (September 7, 2011): 48–58. http://dx.doi.org/10.1002/wics.194.

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20

Kamtchueng, Christian. "Uncertain Monte Carlo." Wilmott 2013, no. 66 (July 2013): 54–63. http://dx.doi.org/10.1002/wilm.10234.

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21

Soisson, F. "Monte Carlo Simulations." EPJ Web of Conferences 14 (2011): 02003. http://dx.doi.org/10.1051/epjconf/20111402003.

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22

Glasserman, Paul. "Filtered Monte Carlo." Mathematics of Operations Research 18, no. 3 (August 1993): 610–34. http://dx.doi.org/10.1287/moor.18.3.610.

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23

CEPERLEY, D., and B. ALDER. "Quantum Monte Carlo." Science 231, no. 4738 (February 7, 1986): 555–60. http://dx.doi.org/10.1126/science.231.4738.555.

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24

Newton, Paul K., and Kamran Aslam. "Monte Carlo Tennis." SIAM Review 48, no. 4 (January 2006): 722–42. http://dx.doi.org/10.1137/050640278.

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25

Dickman, B. H., and M. J. Gilman. "Monte Carlo optimization." Journal of Optimization Theory and Applications 60, no. 1 (January 1989): 149–57. http://dx.doi.org/10.1007/bf00938806.

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26

Koch, Karl-Rudolf. "Monte Carlo methods." GEM - International Journal on Geomathematics 9, no. 1 (December 5, 2017): 117–43. http://dx.doi.org/10.1007/s13137-017-0101-z.

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27

Duane, Simon, A. D. Kennedy, Brian J. Pendleton, and Duncan Roweth. "Hybrid Monte Carlo." Physics Letters B 195, no. 2 (September 1987): 216–22. http://dx.doi.org/10.1016/0370-2693(87)91197-x.

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28

Neumann, L. "Monte Carlo radiosity." Computing 55, no. 1 (March 1995): 23–42. http://dx.doi.org/10.1007/bf02238235.

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29

Kennedy, A. D. "Hybrid Monte Carlo." Nuclear Physics B - Proceedings Supplements 4 (April 1988): 576–79. http://dx.doi.org/10.1016/0920-5632(88)90157-0.

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30

Youssef, S. "Monte carlo techniques." European Physical Journal C 15, no. 1-4 (March 2000): 202–4. http://dx.doi.org/10.1007/bf02683425.

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31

Ziegel, Eric R., H. Niederreiter, and P. Shiue. "Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing." Technometrics 38, no. 4 (November 1996): 414. http://dx.doi.org/10.2307/1271337.

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32

Münger, E. P., and M. A. Novotny. "Reweighting in Monte Carlo and Monte Carlo renormalization-group studies." Physical Review B 43, no. 7 (March 1, 1991): 5773–83. http://dx.doi.org/10.1103/physrevb.43.5773.

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33

Nilmeier, Jerome, and Matthew P. Jacobson. "Monte Carlo Sampling with Hierarchical Move Sets: POSH Monte Carlo." Journal of Chemical Theory and Computation 5, no. 8 (July 20, 2009): 1968–84. http://dx.doi.org/10.1021/ct8005166.

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34

Kleiss, Ronald, and Achilleas Lazopoulos. "Error in Monte Carlo, quasi-error in Quasi-Monte Carlo." Computer Physics Communications 175, no. 2 (July 2006): 93–115. http://dx.doi.org/10.1016/j.cpc.2006.02.001.

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35

Bassen, A., A. Lemke, and H. Bertagnolli. "Monte Carlo and reverse Monte Carlo simulations on molten zinc chloride." Physical Chemistry Chemical Physics 2, no. 7 (2000): 1445–54. http://dx.doi.org/10.1039/a907592e.

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36

Lee, Dean. "The role of Monte Carlo within a diagonalization/Monte Carlo scheme." Nuclear Physics B - Proceedings Supplements 94, no. 1-3 (March 2001): 809–12. http://dx.doi.org/10.1016/s0920-5632(01)01011-8.

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37

South, L. F., A. N. Pettitt, and C. C. Drovandi. "Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals." Bayesian Analysis 14, no. 3 (September 2019): 753–76. http://dx.doi.org/10.1214/18-ba1129.

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38

Coulibaly, N., and B. Wade Brorsen. "Monte carlo sampling approach to testing nonnested hypothesis: monte carlo results." Econometric Reviews 18, no. 2 (January 1999): 195–209. http://dx.doi.org/10.1080/07474939908800439.

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39

Kentel, E., and M. M. Aral. "2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment." Stochastic Environmental Research and Risk Assessment 19, no. 1 (February 2005): 86–96. http://dx.doi.org/10.1007/s00477-004-0209-1.

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40

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 (March 1, 2021): 012053. http://dx.doi.org/10.1088/1742-6596/1821/1/012053.

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41

Owen, Alexander M. "The Monte Carlo fallacy." Medical Journal of Australia 195, no. 7 (October 2011): 421. http://dx.doi.org/10.5694/mja11.10937.

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42

Adler, Jost, and Shahrzad Kurbiel. "Markov Chain Monte Carlo." WiSt - Wirtschaftswissenschaftliches Studium 44, no. 5 (2015): 238–45. http://dx.doi.org/10.15358/0340-1650-2015-5-238.

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43

Bayer, Kristina, Sebastian Koch, and Robert Klein. "Monte Carlo Tree Search." WiSt - Wirtschaftswissenschaftliches Studium 47, no. 12 (2018): 11–18. http://dx.doi.org/10.15358/0340-1650-2018-12-11.

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44

Iba, Yukito. "Population Monte Carlo algorithms." Transactions of the Japanese Society for Artificial Intelligence 16 (2001): 279–86. http://dx.doi.org/10.1527/tjsai.16.279.

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45

Demsyn-Jones, Richard. "Misadventures in Monte Carlo." Journal of Sports Analytics 5, no. 1 (March 1, 2019): 1–15. http://dx.doi.org/10.3233/jsa-170220.

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46

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 (November 25, 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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47

Janke, Wolfhard. "Multicanonical Monte Carlo simulations." Physica A: Statistical Mechanics and its Applications 254, no. 1-2 (May 1998): 164–78. http://dx.doi.org/10.1016/s0378-4371(98)00014-4.

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48

Xu, Qing-Song, and Yi-Zeng Liang. "Monte Carlo cross validation." Chemometrics and Intelligent Laboratory Systems 56, no. 1 (April 2001): 1–11. http://dx.doi.org/10.1016/s0169-7439(00)00122-2.

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49

Sminchisescu, Cristian, and Max Welling. "Generalized darting Monte Carlo." Pattern Recognition 44, no. 10-11 (October 2011): 2738–48. http://dx.doi.org/10.1016/j.patcog.2011.02.006.

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50

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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