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Journal articles on the topic 'Simulation en méthode Monte Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Sambou, S. "Comparaison par simulation de Monte-Carlo des propriétés de deux estimateurs du paramètre d'échelle de la loi exponentielle : méthode du maximum de vraisemblance (MV) et méthode des moindres carrés (MC)." Revue des sciences de l'eau 17, no. 1 (2005): 23–47. http://dx.doi.org/10.7202/705521ar.

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La loi exponentielle est très répandue en hydrologie : elle est faiblement paramétrée, de mise en œuvre aisée. Deux méthodes sont fréquemment utilisées pour estimer son paramètre : la méthode du maximum de vraisemblance et la méthode des moments, qui fournissent la même estimation. A côté de ces deux méthodes, il y a celle des moindres carrés qui est très rarement utilisée pour cette loi. Dans cet article, nous comparons le comportement asymptotique de l'estimateur de la méthode des moindres carrés avec celui de la méthode du maximum de vraisemblance en partant d'une loi exponentielle à un seu

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2

Moulin, A., J. Henry, and E. Lemaire. "Simulation de la texture de recuit d’aciers IF par la méthode de Monte-Carlo." Revue de Métallurgie 91, no. 9 (1994): 1252. http://dx.doi.org/10.1051/metal/199491091252.

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3

Tanguy, D., and T. Magnin. "Piégeage de l’hydrogène aux joints de grains dans Al-5Mg : simulation par la méthode Monte Carlo." Matériaux & Techniques 88, no. 9-10 (2000): 45–48. http://dx.doi.org/10.1051/mattech/200088090045.

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4

-Inguimbert, Christophe. "Simulation par la méthode de Monte Carlo du taux de basculement logique induit par les protons." Revue de l'Electricité et de l'Electronique -, no. 10 (1997): 71. http://dx.doi.org/10.3845/ree.1997.129.

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5

Martinie, B., J. Lecomte, J. Lebreton, and N. Ben Kaddour. "Simulation de la transition de phase orthorhombiquequadratique de YBa2Cu3O7-x par la méthode de Monte-Carlo." Journal de Physique III 1, no. 11 (1991): 1787–94. http://dx.doi.org/10.1051/jp3:1991233.

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6

Senou, Marcel, and L. Dempfle. "Simulation Monte-Carlo pour évaluer l’impact des schémas MOET adultes chez les bovins Somba." Revue d’élevage et de médecine vétérinaire des pays tropicaux 61, no. 2 (2008): 115. http://dx.doi.org/10.19182/remvt.9997.

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L’impact de la technique de superovulation et du transfert d’embryons (MOET) sur la performance de la race Somba a été évalué à l’aide de simulations du type Monte-Carlo. Des schémas MOET adultes en noyaux fermés ont été simulés et soumis à 20 générations consécutives de sélection en supposant une capacité fixe de testage de 512 femelles connues pour leurs performances laitières, un taux de succès de 70 p. 100 pour le transfert, un taux de survie de 70 p. 100 chez les embryons et des tailles variables de familles (nD = 4, 8, 16). Les valeurs additives génétiques des candidats ont été estimées

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7

Barreau, A., J. Berrue, A. Chave, B. Dumon, and M. Thibeau. "Simulation des fluides isotropes par un réseau C.F.C. á l'aide d'une méthode de Monte-Carlo. Application á la diffusion collisionnelle induite." Physica B+C 142, no. 1 (1986): 133–43. http://dx.doi.org/10.1016/0378-4363(86)90233-0.

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8

Lubes-Niel, H., J. M. Masson, J. E. Paturel, and E. Servat. "Variabilité climatique et statistiques. Etude par simulation de la puissance et de la robustesse de quelques tests utilisés pour vérifier l'homogénéité de chroniques." Revue des sciences de l'eau 11, no. 3 (2005): 383–408. http://dx.doi.org/10.7202/705313ar.

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L'analyse statistique de séries chronologiques de données hydrométéorologiques est un des outils d'identification de variations climatiques. Cette analyse consiste le plus souvent à la mise en œuvre et à l'interprétation de tests statistiques d'homogénéité des séries. Les séries hydrologiques (données de pluie ou de débit) se caractérisent fréquemment par des effectifs faibles, et ne répondent que rarement aux conditions requises par l'application des tests statistiques dont certains sont paramétriques. Nous avons cherché à évaluer, en terme de puissance et de robustesse, le comportement de qu

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9

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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10

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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11

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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12

Jourdain, Benjamin, and Laurent Nguyen. "Minimisation de l'entropie relative par méthode de Monte-Carlo." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 332, no. 4 (2001): 345–50. http://dx.doi.org/10.1016/s0764-4442(01)01835-3.

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13

Nakagawa, Kenji. "Basics of Monte Carlo Simulation." IEICE Communications Society Magazine 2008, no. 6 (2008): 6_11–6_20. http://dx.doi.org/10.1587/bplus.2008.6_11.

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14

Jaenisch, G. R., C. Bellon, M. Zhukovsky, and S. Podoliako. "Monte-Carlo-Simulation und CAD." Materials Testing 47, no. 4 (2005): 210–18. http://dx.doi.org/10.3139/120.100650.

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15

Kiviet, Jan F. "Monte Carlo Simulation for Econometricians." Foundations and Trends® in Econometrics 5, no. 1-2 (2011): 1–181. http://dx.doi.org/10.1561/0800000011.

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16

Schmidt, Rainer. "Monte Carlo simulation of bioadhesion." International Biodeterioration & Biodegradation 40, no. 1 (1997): 29–36. http://dx.doi.org/10.1016/s0964-8305(97)00059-0.

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17

Pradlwarter, H. J., and G. I. Schuëller. "Local Domain Monte Carlo Simulation." Structural Safety 32, no. 5 (2010): 275–80. http://dx.doi.org/10.1016/j.strusafe.2010.03.009.

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18

Gardner, Robin P., and Lianyan Liu. "Monte Carlo simulation for IRRMA." Applied Radiation and Isotopes 53, no. 4-5 (2000): 837–55. http://dx.doi.org/10.1016/s0969-8043(00)00233-5.

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19

Giles, Michael B. "Multilevel Monte Carlo Path Simulation." Operations Research 56, no. 3 (2008): 607–17. http://dx.doi.org/10.1287/opre.1070.0496.

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20

Wang, Jian-Sheng, and Robert H. Swendsen. "Replica Monte Carlo Simulation (Revisited)." Progress of Theoretical Physics Supplement 157 (2005): 317–23. http://dx.doi.org/10.1143/ptps.157.317.

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21

Lipinski, Hans-Gerd, and Gerald Küther. "Monte-Carlo Simulation spinaler Motoneuronausfälle." Biomedizinische Technik/Biomedical Engineering 39, s1 (1994): 350–51. http://dx.doi.org/10.1515/bmte.1994.39.s1.350.

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22

Heringa, J. R., and H. W. J. Blöte. "Geometric cluster Monte Carlo simulation." Physical Review E 57, no. 5 (1998): 4976–78. http://dx.doi.org/10.1103/physreve.57.4976.

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23

Campostrini, Massimo, Paolo Rossi, and Ettore Vicari. "Monte Carlo simulation ofCPN−1models." Physical Review D 46, no. 6 (1992): 2647–62. http://dx.doi.org/10.1103/physrevd.46.2647.

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24

Stauffer, Dietrich. "Monte-Carlo-Simulation mikroskopischer Börsenmodelle." Physik Journal 55, no. 5 (1999): 49–51. http://dx.doi.org/10.1002/phbl.19990550511.

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25

Kuhl, Nelson M. "Monte Carlo Simulation of Transport." Journal of Computational Physics 129, no. 1 (1996): 170–80. http://dx.doi.org/10.1006/jcph.1996.0241.

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26

de Groot, Paul F. M., Albertus H. Bril, Frans J. T. Floris, and A. Ewan Campbell. "Monte Carlo simulation of wells." GEOPHYSICS 61, no. 3 (1996): 631–38. http://dx.doi.org/10.1190/1.1443992.

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We present a method to simulate wells, i.e., 1-D stratigraphic profiles with attached physical properties but without spatial information, using a combination of geological knowledge and Monte Carlo statistics. The simulated data is intended to be used in seismic lateral prediction studies. Our algorithm simulates correlated stochastic variables one by one. There are two major advantages in this approach above the conventional way in which all correlated stochastic vectors are drawn simultaneously. The first advantage is that we can steer the algorithm with rules based on geological reasoning.

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27

Creutz, Michael. "Overrelaxation and Monte Carlo simulation." Physical Review D 36, no. 2 (1987): 515–19. http://dx.doi.org/10.1103/physrevd.36.515.

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28

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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29

Creutz, Michael. "Microcanonical cluster Monte Carlo simulation." Physical Review Letters 69, no. 7 (1992): 1002–5. http://dx.doi.org/10.1103/physrevlett.69.1002.

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30

Uyeno, Dean. "Monte Carlo simulation on microcomputers." SIMULATION 58, no. 6 (1992): 418–23. http://dx.doi.org/10.1177/003754979205800611.

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31

Förster, Stefan. "Monte Carlo-Simulation korrelierter Zufallsvariablen." Blätter der DGVFM 23, no. 3 (1998): 305–11. http://dx.doi.org/10.1007/bf02808293.

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32

Schulze, Tim P. "Efficient kinetic Monte Carlo simulation." Journal of Computational Physics 227, no. 4 (2008): 2455–62. http://dx.doi.org/10.1016/j.jcp.2007.10.021.

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33

Fujibuchi, Toshioh, and Akihiko Takahashi. "9. Application of the Monte Carlo Simulation 6: Monte Carlo Simulation in Nuclear Medicine." Japanese Journal of Radiological Technology 71, no. 5 (2015): 460–67. http://dx.doi.org/10.6009/jjrt.2015_jsrt_71.5.460.

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34

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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35

Murthy, A. Sampath Dakshina. "Noise Cancellation in Monte Carlo Simulation." Indian Journal of Science and Technology 9, no. 1 (2016): 1–4. http://dx.doi.org/10.17485/ijst/2016/v9i31/81631.

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36

Zhou, Kun. "Monte Carlo simulation for soot dynamics." Thermal Science 16, no. 5 (2012): 1391–94. http://dx.doi.org/10.2298/tsci1205391z.

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A new Monte Carlo method termed Comb-like frame Monte Carlo is developed to simulate the soot dynamics. Detailed stochastic error analysis is provided. Comb-like frame Monte Carlo is coupled with the gas phase solver Chemkin II to simulate soot formation in a 1-D premixed burner stabilized flame. The simulated soot number density, volume fraction, and particle size distribution all agree well with the measurement available in literature. The origin of the bimodal distribution of particle size distribution is revealed with quantitative proof.

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37

KATO, Hideki. "Monte Carlo Simulation by Personal Computers." Japanese Journal of Radiological Technology 55, no. 2 (1999): 190–94. http://dx.doi.org/10.6009/jjrt.kj00001356610.

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38

Murtha, Jim. "Some Challenges for Monte Carlo Simulation." Way Ahead 02, no. 02 (2006): 13–18. http://dx.doi.org/10.2118/0206-013-twa.

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39

Blikstein, Paulo, and André Paulo Tschiptschin. "Monte Carlo simulation of grain growth." Materials Research 2, no. 3 (1999): 133–37. http://dx.doi.org/10.1590/s1516-14391999000300004.

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40

Schmidt, R. "Monte-Carlo-Simulation of Precipitation Phenomena." Materials Science Forum 62-64 (January 1991): 659–62. http://dx.doi.org/10.4028/www.scientific.net/msf.62-64.659.

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41

Martinkutė-Kaulienė, Raimonda, Jelena Stankevičienė, and Santautė Žinytė. "OPTION PRICING USING MONTE CARLO SIMULATION." Journal of Security and Sustainability Issues 2, no. 4 (2013): 65–79. http://dx.doi.org/10.9770/jssi.2013.2.4(7).

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42

Suzuki, Masafumi, Noboru Yoshimura, Osamu Kimura, and Masanobu Awata. "Monte Carlo simulation for luminous exitance." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 77, Appendix (1993): 82. http://dx.doi.org/10.2150/jieij1980.77.appendix_82.

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43

de Oliveira, Paulo M. C., Suzana Moss de Oliveira, Americo T. Bernardes, and Dietrich Stauffer. "Monte Carlo simulation of inherited longevity." Physica A: Statistical Mechanics and its Applications 262, no. 1-2 (1999): 242–48. http://dx.doi.org/10.1016/s0378-4371(98)00413-0.

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44

Tan, S., A. Ghazali, and J. C. S. Lévy. "Monte-Carlo simulation of surface superstructures." Surface Science 377-379 (April 1997): 15–17. http://dx.doi.org/10.1016/s0039-6028(96)01319-2.

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45

Saito, Yoshiyuki, and Masato Enomoto. "Monte Carlo Simulation of Grain Growth." ISIJ International 32, no. 3 (1992): 267–74. http://dx.doi.org/10.2355/isijinternational.32.267.

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46

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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47

Creutz, Michael, and Rajiv Gavai. "Monte Carlo simulation of fermionic fields." Nuclear Physics B 280 (January 1987): 181–91. http://dx.doi.org/10.1016/0550-3213(87)90143-x.

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48

Cogan, James L. "Monte Carlo simulation of buoyant dispersion." Atmospheric Environment (1967) 19, no. 6 (1985): 867–78. http://dx.doi.org/10.1016/0004-6981(85)90232-x.

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49

Solomon, Frederick. "Monte Carlo Simulation of Infinite Series." Mathematics Magazine 64, no. 3 (1991): 188. http://dx.doi.org/10.2307/2691302.

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50

Nie, Lixing, Wuli Yang, Hongdong Zhang, and Shoukuan Fu. "Monte Carlo simulation of microemulsion polymerization." Polymer 46, no. 9 (2005): 3175–84. http://dx.doi.org/10.1016/j.polymer.2005.01.085.

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