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Journal articles on the topic 'Monte Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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

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2

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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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 (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 a

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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 (2020): 227–32. http://dx.doi.org/10.18178/ijmlc.2020.10.2.924.

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6

Chen, Jiming. "Monte Carlo Simulations in Complex Systems: Challenges and New Approaches." Theoretical and Natural Science 86, no. 1 (2025): 114–19. https://doi.org/10.54254/2753-8818/2025.20172.

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Monte Carlo simulations are crucial for examining the Ising model, especially when it's tough to find analytical solutions. However, traditional Monte Carlo methods, like the Metropolis algorithm, encounter significant hurdles, such as slowing down near phase transitions and issues related to finite sizes. This paper looks into both the advantages and limitations of these traditional Monte Carlo techniques. It also covers recent developments like Tensor Network Monte Carlo and Quantum Monte Carlo methods, which have shown promise in overcoming these challenges. Furthermore, the paper explores

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7

Todorov, Venelin. "COMPUTING HIGH DIMENSIONAL INTEGRALS WITH MONTE CARLO METHODS." Journal Scientific and Applied Research 10, no. 1 (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 v

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8

Galin, A. V., P. S. Rudny, and K. A. Galin. "Monte-Carlo analysis model for evaluation of container terminal parameters." Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova 16, no. 6 (2025): 837–46. https://doi.org/10.21821/2309-5180-2024-16-6-837-846.

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This paper considers using Monte-Carlo analysis method for evaluation some of the parameters of a container terminal. A high amount of scientific work on this topic is noted in domestic literature. International scientific literature concerning usage of Monte-Carlo method for simulating different parameters of container terminals is also analyzed. We note that foreign authors often use Monte-Carlo analysis as an auxiliary method, for example, for checking results of discrete-event simulation model of a complicated logistical system for adequacy, whereas domestic authors often use Monte-Carlo a

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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

Tesauro, Gerald. "MONTE-CARLO BACKGAMMON." ICGA Journal 30, no. 3 (2007): 183. http://dx.doi.org/10.3233/icg-2007-30317.

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11

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

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12

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

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13

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

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14

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

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15

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

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16

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

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17

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

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18

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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19

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

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20

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

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21

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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22

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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23

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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24

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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25

Dai, Hongsheng, Murray Pollock, and Gareth Roberts. "Monte Carlo fusion." Journal of Applied Probability 56, no. 01 (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 u

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26

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

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27

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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28

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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29

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

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30

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

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31

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

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32

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

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33

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

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34

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

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35

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

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36

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

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37

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

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38

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

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39

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

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40

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 (2009): 1968–84. http://dx.doi.org/10.1021/ct8005166.

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41

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

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42

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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43

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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44

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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45

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

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46

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 (2019): 753–76. http://dx.doi.org/10.1214/18-ba1129.

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47

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

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48

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 (2005): 86–96. http://dx.doi.org/10.1007/s00477-004-0209-1.

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49

IBA, YUKITO. "EXTENDED ENSEMBLE MONTE CARLO." International Journal of Modern Physics C 12, no. 05 (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 Carl

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50

NIEDERREITER, HARALD. "QUASI-MONTE CARLO METHODS IN COMPUTATIONAL FINANCE." COSMOS 01, no. 01 (2005): 113–25. http://dx.doi.org/10.1142/s0219607705000097.

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Quasi-Monte Carlo methods are deterministic versions of Monte Carlo methods, in the sense that the random samples used in the implementation of a Monte Carlo method are replaced by judiciously chosen deterministic points with good distribution properties. They outperform classical Monte Carlo methods in many problems of scientific computing. This paper discusses applications of quasi-Monte Carlo methods to computational finance, with a special emphasis on the problems of pricing mortgage-backed securities and options. The necessary background on Monte Carlo and quasi-Monte Carlo methods is als

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