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Articoli di riviste sul tema "Monte Carlo method"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 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

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ohta, Shigemi. "Self-Test Monte Carlo Method." Progress of Theoretical Physics Supplement 122 (1996): 193–200. http://dx.doi.org/10.1143/ptps.122.193.

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12

Wang, Jian-Sheng. "Flat Histogram Monte Carlo Method." Progress of Theoretical Physics Supplement 138 (2000): 454–55. http://dx.doi.org/10.1143/ptps.138.454.

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13

Beichl, I., and F. Sullivan. "The other Monte Carlo method." Computing in Science & Engineering 8, no. 2 (2006): 42–47. http://dx.doi.org/10.1109/mcse.2006.35.

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14

Wang, Jian-Sheng. "Flat histogram Monte Carlo method." Physica A: Statistical Mechanics and its Applications 281, no. 1-4 (2000): 147–50. http://dx.doi.org/10.1016/s0378-4371(00)00016-9.

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15

Wang, Jian-Sheng. "Transition matrix Monte Carlo method." Computer Physics Communications 121-122 (September 1999): 22–25. http://dx.doi.org/10.1016/s0010-4655(99)00270-2.

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16

Schaefer, G., and P. Hui. "The Monte Carlo flux method." Journal of Computational Physics 89, no. 1 (1990): 1–30. http://dx.doi.org/10.1016/0021-9991(90)90114-g.

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17

Ray, John R. "Microcanonical ensemble Monte Carlo method." Physical Review A 44, no. 6 (1991): 4061–64. http://dx.doi.org/10.1103/physreva.44.4061.

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18

Oht, Shigemi. "Self-test Monte Carlo method." Nuclear Physics B - Proceedings Supplements 47, no. 1-3 (1996): 788–91. http://dx.doi.org/10.1016/0920-5632(96)00175-2.

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19

Towler, M. D. "The quantum Monte Carlo method." physica status solidi (b) 243, no. 11 (2006): 2573–98. http://dx.doi.org/10.1002/pssb.200642125.

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20

Nekrasova, Mariia. "Monte-Carlo method and artificial intelligence: application of Monte-Carlo method in reinforcement learning." Bulletin of the National Technical University «KhPI» Series: Dynamics and Strength of Machines, no. 2 (December 24, 2024): 47–52. https://doi.org/10.20998/2078-9130.2024.2.315342.

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Abstract (sommario):

Reinforcement learning is the fastest growing technology used in the creation of artificial intelligent systems. At the moment, this field is quite extensive. Many researchers around the world are actively working with reinforcement learning in various fields: neuroscience, control theory, psychology and many others. The purpose of this paper is to substantiate the possibility of using the Monte Carlo method in reinforcement learning. It is known that the main thing in such learning is to record aspects of a real problem when a learner interacts with the surrounding world to achieve his goal.

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21

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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22

Kandidov, V. P. "Monte Carlo method in nonlinear statistical optics." Uspekhi Fizicheskih Nauk 166, no. 12 (1996): 1309. http://dx.doi.org/10.3367/ufnr.0166.199612c.1309.

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23

Lee, Jin-Han, Young-Do Jo, and Lae Hyun Kim. "Reliability Assessment for Corroded Pipelines by Separable Monte Carlo Method." Journal of the Korean Institute of Gas 19, no. 5 (2015): 81–86. http://dx.doi.org/10.7842/kigas.2015.19.5.81.

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24

Puddu, G. "A comparison between the Monte Carlo shell model method and the Monte Carlo spectroscopic method." Journal of Physics G: Nuclear and Particle Physics 29, no. 9 (2003): 2179–85. http://dx.doi.org/10.1088/0954-3899/29/9/312.

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25

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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26

Giles, Michael B. "Multilevel Monte Carlo methods." Acta Numerica 24 (April 27, 2015): 259–328. http://dx.doi.org/10.1017/s096249291500001x.

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Abstract (sommario):

Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost.In this article, we review the ideas beh

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27

Jiajin, Xu. "Using Monte Carlo method to Upgrade Zhentong Gao method." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 633–40. https://doi.org/10.5281/zenodo.14603002.

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In recent decades, it has been found that the three-parament Weibull distribution plays an increasingly important role in the fields such as structural fatigue and reliability. However, the complexity of estimating these three parameters greatly affects the application of the Weibull distribution. In the past three years, the author has successively proposed the Zhentong Gao Method and the Generalized Zhentong Gao Method, which have effectively solved this problem. However, it has also been found that there is room for improvement in these two methods. That's what this paper is about, how to u

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28

Betancourt, Michael. "The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo." Annalen der Physik 531, no. 3 (2018): 1700214. http://dx.doi.org/10.1002/andp.201700214.

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29

An, Nier, and Bocheng Su. "Pricing Options and Monte-Carlo Method a literature Review." BCP Business & Management 44 (April 27, 2023): 29–35. http://dx.doi.org/10.54691/bcpbm.v44i.4789.

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This literature review provides an overview of the past and present of using Monte Carlo methods to price options. From the most B-S model to combining it with a Monte Carlo method, and then from a pricing model to a method for reducing the variance of the Monte Carlo method. Furthermore, building on the solid foundation of the previous research, more recent research has focused on integrating up to several hundred dimensions and even using machine learning methods to price options. This article aims to suggest a traceable path for beginners of Monte Carlo methods, providing them with a direct

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30

Br Manik, Mawar Bonita, Putri Khairiah Nasution, Suyanto Suyanto, and Maulida Yanti. "Kajian Metode Simulasi Monte Carlo." Journal of Mathematics, Computations and Statistics 7, no. 2 (2024): 232–42. http://dx.doi.org/10.35580/jmathcos.v7i2.2994.

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The Monte Carlo Simulation Method is one of the forecasting methods that uses random numbers, specifically through the use of a Linear Congruential Generator and mathematical equations for prediction, forecasting, estimation, and risk analysis. The Monte Carlo Simulation Method with one iteration has a high level of accuracy, as evidenced by previous research. The more iterations used, the more accurate the forecasting results. Therefore, the author is interested in examining how well the Monte Carlo Simulation Method with N iterations performs in forecasting. The study of the Monte Carlo Simu

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31

Ohtani, Yoshihiko, Mamoru Ohkawa, Akira Uchida, and Tetsuo Yamaya. "Illuminance Calculation Using Monte Carlo Method." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 82, no. 2 (1998): 105–11. http://dx.doi.org/10.2150/jieij1980.82.2_105.

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32

Wenger, Trey V., Dana S. Balser, L. D. Anderson, and T. M. Bania. "Kinematic Distances: A Monte Carlo Method." Astrophysical Journal 856, no. 1 (2018): 52. http://dx.doi.org/10.3847/1538-4357/aaaec8.

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33

Ranjbaran, Abdolrasoul, Mohammad Ranjbaran, and Fatema Ranjbaran. "Persian Curve Versus Monte Carlo Method." International Journal of Structural Glass and Advanced Materials Research 5, no. 1 (2021): 234–46. http://dx.doi.org/10.3844/sgamrsp.2021.234.246.

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34

Takahashi, Akihiko, and Nakahiro Yoshida. "Monte Carlo Simulation with Asymptotic Method." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 35, no. 2 (2005): 171–203. http://dx.doi.org/10.14490/jjss.35.171.

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35

Alexander, Francis J., and Alejandro L. Garcia. "The Direct Simulation Monte Carlo Method." Computers in Physics 11, no. 6 (1997): 588. http://dx.doi.org/10.1063/1.168619.

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36

OHTANI, Yoshihiko, Mamoru OHKAWA, Akira UCHIDA, and Tetsuo YAMAYA. "Illuminance Calculation Using Monte Carlo Method." Journal of Light & Visual Environment 24, no. 1 (2000): 42–49. http://dx.doi.org/10.2150/jlve.24.1_42.

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37

Bokor, Nandor. "Monte Carlo method in computer holography." Optical Engineering 36, no. 4 (1997): 1014. http://dx.doi.org/10.1117/1.601294.

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38

Lacasse, Martin-D., Jorge Viñals, and Martin Grant. "Dynamic Monte Carlo renormalization-group method." Physical Review B 47, no. 10 (1993): 5646–52. http://dx.doi.org/10.1103/physrevb.47.5646.

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39

Kröger, Helmut. "Monte Carlo method for scattering reactions." Physical Review A 35, no. 11 (1987): 4526–32. http://dx.doi.org/10.1103/physreva.35.4526.

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40

Jones, Matthew D., Gerardo Ortiz, and David M. Ceperley. "Released-phase quantum Monte Carlo method." Physical Review E 55, no. 5 (1997): 6202–10. http://dx.doi.org/10.1103/physreve.55.6202.

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41

Iskandar, S. "Modified Monte Carlo method for integral." Journal of Physics: Conference Series 1462 (February 2020): 012061. http://dx.doi.org/10.1088/1742-6596/1462/1/012061.

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42

Fernández, L. A., V. Martín-Mayor, and P. Verrocchio. "Optimized Monte Carlo method for glasses." Philosophical Magazine 87, no. 3-5 (2007): 581–86. http://dx.doi.org/10.1080/14786430600919302.

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43

Berg, B., A. Billoire, and D. Foerster. "Monte Carlo method for random surfaces." Nuclear Physics B 251 (January 1985): 665–75. http://dx.doi.org/10.1016/s0550-3213(85)80002-x.

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44

de Lataillade, A., S. Blanco, Y. Clergent, J. L. Dufresne, M. El Hafi, and R. Fournier. "Monte Carlo method and sensitivity estimations." Journal of Quantitative Spectroscopy and Radiative Transfer 75, no. 5 (2002): 529–38. http://dx.doi.org/10.1016/s0022-4073(02)00027-4.

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45

Rota, Gian-Carlo. "Simulation and the Monte-Carlo method." Advances in Mathematics 60, no. 1 (1986): 123. http://dx.doi.org/10.1016/0001-8708(86)90009-5.

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46

Gupta, Rajan, K. G. Wilson, and C. Umrigar. "Improved Monte Carlo renormalization group method." Journal of Statistical Physics 43, no. 5-6 (1986): 1095–99. http://dx.doi.org/10.1007/bf02628333.

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47

Socha, J. B., and J. A. Krumhansl. "The Monte Carlo trajectory integral method." Physica B+C 134, no. 1-3 (1985): 142–47. http://dx.doi.org/10.1016/0378-4363(85)90334-1.

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48

Mackenze, Paul B. "An improved hybrid Monte Carlo method." Physics Letters B 226, no. 3-4 (1989): 369–71. http://dx.doi.org/10.1016/0370-2693(89)91212-4.

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49

Date, Hiroyuki. "1. Principle of Monte Carlo Method." Japanese Journal of Radiological Technology 70, no. 6 (2014): 582–87. http://dx.doi.org/10.6009/jjrt.2014_jsrt_70.6.582.

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Date, Hiroyuki. "2. Monte Carlo Method and Simulation." Japanese Journal of Radiological Technology 70, no. 7 (2014): 705–14. http://dx.doi.org/10.6009/jjrt.2014_jsrt_70.7.705.

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