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Journal articles on the topic 'Monte Carlo'

To see the other types of publications on this topic, follow the link: Monte Carlo.

Author: Grafiati

Published: 4 June 2021

Last updated: 14 September 2024

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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

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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3

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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4

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’ 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’s reasoning is given after a review of some statistical ideas.</p> <p> </p>

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5

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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6

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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7

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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8

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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9

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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10

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

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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

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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

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25

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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26

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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27

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

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28

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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29

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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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

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

Musrati, Mufid Mohamed A., and Hanan Ettaher Dagez. "New Optimized Crossover Utilizes Exponential Monte Carlo." International Journal of Computer and Communication Engineering 3, no. 5 (2014): 384–87. http://dx.doi.org/10.7763/ijcce.2014.v3.354.

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41

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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42

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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43

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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44

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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45

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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46

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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47

IBA, YUKITO. "EXTENDED ENSEMBLE MONTE CARLO." International Journal of Modern Physics C 12, no. 05 (June 2001): 623–56. http://dx.doi.org/10.1142/s0129183101001912.

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"Extended Ensemble Monte Carlo" is a generic term that indicates a set of algorithms, which are now popular in a variety of fields in physics and statistical information processing. Exchange Monte Carlo (Metropolis-Coupled Chain, Parallel Tempering), Simulated Tempering (Expanded Ensemble Monte Carlo) and Multicanonical Monte Carlo (Adaptive Umbrella Sampling) are typical members of this family. Here, we give a cross-disciplinary survey of these algorithms with special emphasis on the great flexibility of the underlying idea. In Sec. 2, we discuss the background of Extended Ensemble Monte Carlo. In Secs. 3, 4 and 5, three types of the algorithms, i.e., Exchange Monte Carlo, Simulated Tempering, Multicanonical Monte Carlo, are introduced. In Sec. 6, we give an introduction to Replica Monte Carlo algorithm by Swendsen and Wang. Strategies for the construction of special-purpose extended ensembles are discussed in Sec. 7. We stress that an extension is not necessary restricted to the space of energy or temperature. Even unphysical (unrealizable) configurations can be included in the ensemble, if the resultant fast mixing of the Markov chain offsets the increasing cost of the sampling procedure. Multivariate (multicomponent) extensions are also useful in many examples. In Sec. 8, we give a survey on extended ensembles with a state space whose dimensionality is dynamically varying. In the appendix, we discuss advantages and disadvantages of three types of extended ensemble algorithms.

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48

Koh, Wook Hee. "Monte Carlo Simulation of Thermionic Low Pressure Discharge Plasma." Transactions of The Korean Institute of Electrical Engineers 61, no. 12 (December 1, 2012): 1880–85. http://dx.doi.org/10.5370/kiee.2012.61.12.1880.

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49

CAZENAVE, TRISTAN. "MONTE-CARLO EXPRESSION DISCOVERY." International Journal on Artificial Intelligence Tools 22, no. 01 (February 2013): 1250035. http://dx.doi.org/10.1142/s0218213012500352.

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Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize.

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50

Мазур, Владимир. "Отель. "Metrohole" Monte-Carlo." Мир туризма, no. 4 (2006): 20–23.

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