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Journal articles on the topic 'Monte Carlo experiments'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Paxton, Pamela, Patrick J. Curran, Kenneth A. Bollen, Jim Kirby, and Feinian Chen. "Monte Carlo Experiments: Design and Implementation." Structural Equation Modeling: A Multidisciplinary Journal 8, no. 2 (April 2001): 287–312. http://dx.doi.org/10.1207/s15328007sem0802_7.

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2

Adkins, Lee C. "Using gretl for Monte Carlo experiments." Journal of Applied Econometrics 26, no. 5 (December 9, 2010): 880–85. http://dx.doi.org/10.1002/jae.1228.

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3

Demeler, Borries, and Emre Brookes. "Monte Carlo analysis of sedimentation experiments." Colloid and Polymer Science 286, no. 2 (June 13, 2007): 129–37. http://dx.doi.org/10.1007/s00396-007-1699-4.

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4

Norén, B., and B. Jakobsson. "Monte Carlo simulations of anomalon experiments." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 17, no. 3 (October 1986): 265–74. http://dx.doi.org/10.1016/0168-583x(86)90066-2.

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5

Anderson, Robert, Zhou Wei, Ian Cox, Malcolm Moore, and Florence Kussener. "Monte Carlo Simulation Experiments for Engineering Optimisation." Studies in Engineering and Technology 2, no. 1 (July 22, 2015): 97. http://dx.doi.org/10.11114/set.v2i1.901.

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Design of Experiments (DoE) is widely used in design, manufacturing and quality management. The resulting data is usually analysed with multiple linear regression to generate polynomial equations that describe the relationship between process inputs and outputs. These equations enable us to understand how input values affect the predicted value of one or more outputs and find good set points for the inputs. However, to develop robust manufacturing processes, we also need to understand how variation in these inputs appears as variation in the output. This understanding allows us to define set points and control tolerances for the inputs that will keep the outputs within their required specification windows. Tolerance analysis provides a powerful way of finding input settings and ranges that minimise output variation to produce a process that is robust. In many practical applications, tolerance analysis exploits Monte Carlo simulation of the polynomial model generated from DoE’s. This paper briefly describes tolerance analysis and then shows how Monte Carlo simulation experiments using space-filling designs can be used to find the input settings that result in a robust process. Using this approach, engineers can quickly and easily identify the key inputs responsible for transferring undesired variation to their process outputs and identify the set points and ranges that make their process as robust as possible. If the process is not sufficiently robust, they can rationally investigate different strategies to improve it. A case study approach is used to aid explanation and understanding.

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6

Zhang, Ji, and Dennis D. Boos. "Adjusted power estimates in monte carlo experiments." Communications in Statistics - Simulation and Computation 23, no. 1 (January 1994): 165–73. http://dx.doi.org/10.1080/03610919408813162.

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7

Leemis, Lawrence, and Bruce Schmeiser. "Random Variate Generation for Monte Carlo Experiments." IEEE Transactions on Reliability R-34, no. 1 (April 1985): 81–85. http://dx.doi.org/10.1109/tr.1985.5221941.

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8

Fishman, George S., and David S. Rubin. "Bounding the variance in Monte Carlo experiments." Operations Research Letters 11, no. 4 (May 1992): 243–48. http://dx.doi.org/10.1016/0167-6377(92)90031-w.

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9

Feng, Mingbin, and Jeremy Staum. "Green Simulation with Database Monte Carlo." ACM Transactions on Modeling and Computer Simulation 31, no. 1 (February 2021): 1–26. http://dx.doi.org/10.1145/3429336.

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In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed computational budget, and then idle time is available for further experimentation before the next question is asked. The general strategy is database Monte Carlo for green simulation: the output of experiments is stored in a database and used to improve the computational efficiency of future experiments. In this article, the database provides a quasi-control variate, which reduces the variance of the estimated mean response in a future experiment that has a fixed computational budget. We propose a particular green simulation procedure using quasi-control variates, addressing practical issues such as experiment design, and analyze its theoretical properties. We show that, under some conditions, the variance of the estimated mean response in an experiment with a fixed computational budget drops to zero over a sequence of repeated experiments, as more and more idle time is invested in creating databases. Our numerical experiments on the procedure show that using idle time to create databases of simulation output provides variance reduction immediately, and that the variance reduction grows over time in a way that is consistent with the convergence analysis.

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10

Westland, Stephen, Yuan Li, and Vien Cheung. "Monte Carlo Analysis of Incomplete Paired-Comparison Experiments." Journal of Imaging Science and Technology 58, no. 5 (September 1, 2014): 505061–66. http://dx.doi.org/10.2352/j.imagingsci.technol.2014.58.5.050506.

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11

Willemsen, Jorge F. "Monte Carlo data processing for invasion-percolation experiments." Physical Review A 31, no. 1 (January 1, 1985): 432–38. http://dx.doi.org/10.1103/physreva.31.432.

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12

Fischer, U., R. L. Perel, and H. Tsige-Tamirat. "Monte Carlo uncertainty analyses for integral beryllium experiments." Fusion Engineering and Design 51-52 (November 2000): 761–68. http://dx.doi.org/10.1016/s0920-3796(00)00232-5.

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13

Kirihara, Y., Y. Namito, H. Iwase, and H. Hirayama. "Monte Carlo simulation of Tabata’s electron backscattering experiments." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 268, no. 15 (August 2010): 2384–90. http://dx.doi.org/10.1016/j.nimb.2009.12.014.

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14

Aoki, Satoshi, and Akimichi Takemura. "Markov chain Monte Carlo tests for designed experiments." Journal of Statistical Planning and Inference 140, no. 3 (March 2010): 817–30. http://dx.doi.org/10.1016/j.jspi.2009.09.010.

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15

Arsham, H. "On the inverse problem in Monte Carlo experiments." Inverse Problems 5, no. 6 (December 1, 1989): 927–34. http://dx.doi.org/10.1088/0266-5611/5/6/004.

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16

Arpesella, C., E. Bellotti, C. Broggini, P. Corvisiero, S. Fubini, G. Gervino, U. Greife, et al. "A Monte Carlo code for nuclear astrophysics experiments." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 360, no. 3 (June 1995): 607–15. http://dx.doi.org/10.1016/0168-9002(95)00038-0.

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17

Dettrick, S. A., H. J. Gardner, and S. L. Painter. "Monte Carlo Transport Simulation Techniques for Stellarator Fusion Experiments." Australian Journal of Physics 52, no. 4 (1999): 715. http://dx.doi.org/10.1071/ph98106.

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We describe an implementation of a particle orbit-following simulation approach to the Monte Carlo calculation of neoclassical transport coecients which has been developed for application to the H-1NF Heliac. We compare and contrast some Monte Carlo transport coecient estimators that can be used in such computer codes, from both physical and computational perspectives, and we make recommendations for their use. Transport coecient calculations are performed for the H-1NF in conditions that will be available after the full National Facility upgrade.

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18

Chekanov, S. V. "HepSim: A Repository with Predictions for High-Energy Physics Experiments." Advances in High Energy Physics 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/136093.

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A file repository for calculations of cross sections and kinematic distributions using Monte Carlo generators for high-energy collisions is discussed. The repository is used to facilitate effective preservation and archiving of data from theoretical calculations and for comparisons with experimental data. The HepSim data library is publicly accessible and includes a number of Monte Carlo event samples with Standard Model predictions for current and future experiments. The HepSim project includes a software package to automate the process of downloading and viewing online Monte Carlo event samples. Data streaming over a network for end-user analysis is discussed.

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19

Muller, Peter, and Giovanni Parmigiani. "Optimal Design via Curve Fitting of Monte Carlo Experiments." Journal of the American Statistical Association 90, no. 432 (December 1995): 1322. http://dx.doi.org/10.2307/2291522.

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20

Winkler, L. I., and C. E. Goldblum. "Monte Carlo integration to optimize geometry in gravitational experiments." Review of Scientific Instruments 63, no. 7 (July 1992): 3556–63. http://dx.doi.org/10.1063/1.1143764.

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21

Shahmohammadi Beni, Mehrdad, Tak Cheong Hau, D. Krstic, D. Nikezic, and K. N. Yu. "Monte Carlo studies on neutron interactions in radiobiological experiments." PLOS ONE 12, no. 7 (July 13, 2017): e0181281. http://dx.doi.org/10.1371/journal.pone.0181281.

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22

Shahmohammadi Beni, Mehrdad, D. Krstic, D. Nikezic, and K. N. Yu. "Monte Carlo studies on photon interactions in radiobiological experiments." PLOS ONE 13, no. 3 (March 21, 2018): e0193575. http://dx.doi.org/10.1371/journal.pone.0193575.

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23

Beisbart, Claus, and John D. Norton. "Why Monte Carlo Simulations Are Inferences and Not Experiments." International Studies in the Philosophy of Science 26, no. 4 (December 2012): 403–22. http://dx.doi.org/10.1080/02698595.2012.748497.

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24

Jamil, Khalid, Muhammad Kamran, Ahsan Illahi, and Shahid Manzoor. "Monte Carlo simulation experiments on box-type radon dosimeter." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 764 (November 2014): 18–23. http://dx.doi.org/10.1016/j.nima.2014.07.015.

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25

Bencardino, Raffaele, Greg Roach, James Tickner, and Josef Uher. "Efficient Monte Carlo simulation of delayed activation analysis experiments." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 268, no. 5 (March 2010): 513–18. http://dx.doi.org/10.1016/j.nimb.2009.11.008.

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26

Guo, Zhixiong, Janice Aber, Bruce A. Garetz, and Sunil Kumar. "Monte Carlo simulation and experiments of pulsed radiative transfer." Journal of Quantitative Spectroscopy and Radiative Transfer 73, no. 2-5 (April 2002): 159–68. http://dx.doi.org/10.1016/s0022-4073(01)00203-5.

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27

Aleksandrov, V. M., and S. B. Sanina. "Use of Monte Carlo method in planning expensive experiments." Soviet Atomic Energy 66, no. 3 (March 1989): 188–95. http://dx.doi.org/10.1007/bf01123684.

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28

Vos, M., and M. Bottema. "Monte Carlo simulations of (e,2e) experiments on solids." Physical Review B 54, no. 8 (August 15, 1996): 5946–54. http://dx.doi.org/10.1103/physrevb.54.5946.

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29

Müller, Peter, and Giovanni Parmigiani. "Optimal Design via Curve Fitting of Monte Carlo Experiments." Journal of the American Statistical Association 90, no. 432 (December 1995): 1322–30. http://dx.doi.org/10.1080/01621459.1995.10476636.

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30

Simonton, D. K. "Multiple discovery: Some Monte Carlo simulations and Gedanken experiments." Scientometrics 9, no. 5-6 (May 1986): 269–80. http://dx.doi.org/10.1007/bf02017248.

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31

Mern, John, Anil Yildiz, Zachary Sunberg, Tapan Mukerji, and Mykel J. Kochenderfer. "Bayesian Optimized Monte Carlo Planning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11880–87. http://dx.doi.org/10.1609/aaai.v35i13.17411.

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Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree. The performance of progressive widening search is dependent upon the action sampling policy, often requiring problem-specific samplers. In this work, we present a general method for efficient action sampling based on Bayesian optimization. The proposed method uses a Gaussian process to model a belief over the action-value function and selects the action that will maximize the expected improvement in the optimal action value. We implement the proposed approach in a new online tree search algorithm called Bayesian Optimized Monte Carlo Planning (BOMCP). Several experiments show that BOMCP is better able to scale to large action space POMDPs than existing state-of-the-art tree search solvers.

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32

Xiong, Haoyi, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, and Jun Huan. "SpHMC: Spectral Hamiltonian Monte Carlo." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5516–24. http://dx.doi.org/10.1609/aaai.v33i01.33015516.

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Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) methods have been widely used to sample from certain probability distributions, incorporating (kernel) density derivatives and/or given datasets. Instead of exploring new samples from kernel spaces, this piece of work proposed a novel SGHMC sampler, namely Spectral Hamiltonian Monte Carlo (SpHMC), that produces the high dimensional sparse representations of given datasets through sparse sensing and SGHMC. Inspired by compressed sensing, we assume all given samples are low-dimensional measurements of certain high-dimensional sparse vectors, while a continuous probability distribution exists in such high-dimensional space. Specifically, given a dictionary for sparse coding, SpHMC first derives a novel likelihood evaluator of the probability distribution from the loss function of LASSO, then samples from the high-dimensional distribution using stochastic Langevin dynamics with derivatives of the logarithm likelihood and Metropolis–Hastings sampling. In addition, new samples in low-dimensional measuring spaces can be regenerated using the sampled high-dimensional vectors and the dictionary. Extensive experiments have been conducted to evaluate the proposed algorithm using real-world datasets. The performance comparisons on three real-world applications demonstrate the superior performance of SpHMC beyond baseline methods.

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33

Rougier, Jonathan, and David M. H. Sexton. "Inference in ensemble experiments." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1857 (June 14, 2007): 2133–43. http://dx.doi.org/10.1098/rsta.2007.2071.

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We consider inference based on ensembles of climate model evaluations, and contrast the Monte Carlo approach, in which the evaluations are selected at random from the model-input space, with a more overtly statistical approach using emulators and experimental design.

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34

Che, X., and S. Xu. "Bayesian data analysis for agricultural experiments." Canadian Journal of Plant Science 90, no. 5 (September 1, 2010): 575–603. http://dx.doi.org/10.4141/cjps10004.

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Data collected in agricultural experiments can be analyzed in many different ways using different models. The most commonly used models are the linear model and the generalized linear model. The maximum likelihood method is often used for data analysis. However, this method may not be able to handle complicated models, especially multiple level hierarchical models. The Bayesian method partitions complicated models into simple components, each of which may be formulated analytically. Therefore, the Bayesian method is capable of handling very complicated models. The Bayesian method itself may not be more complicated than the maximum likelihood method, but the analysis is time consuming, because numerical integration involved in Bayesian analysis is almost exclusively accomplished based on Monte Carlo simulations, the so called Markov Chain Monte Carlo (MCMC) algorithm. Although the MCMC algorithm is intuitive and straightforward to statisticians, it may not be that simple to agricultural scientists, whose main purpose is to implement the method and interpret the results. In this review, we provide the general concept of Bayesian analysis and the MCMC algorithm in a way that can be understood by non-statisticians. We also demonstrate the implementation of the MCMC algorithm using professional software packages such as the MCMC procedure in SAS software. Three datasets from agricultural experiments were analyzed to demonstrate the MCMC algorithm.Key words: Bayesian method, Generalized linear model, Markov Chain Monte Carlo, SAS, WinBUGS

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35

Galoyan, A. S., and V. V. Uzhinsky. "Monte Carlo event generators for NICA/MPD and CBM experiments." Bulletin of the Russian Academy of Sciences: Physics 80, no. 3 (March 2016): 333–37. http://dx.doi.org/10.3103/s1062873816030138.

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36

Leigh, Jessica W., and David Bryant. "Monte Carlo Strategies for Selecting Parameter Values in Simulation Experiments." Systematic Biology 64, no. 5 (May 25, 2015): 741–51. http://dx.doi.org/10.1093/sysbio/syv030.

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37

Clements, Aspen R., Ilsa Cooke, and Robin T. Garrod. "Monte Carlo Modeling of Astrophysically-Relevant Temperature-Programmed Desorption Experiments." Proceedings of the International Astronomical Union 13, S332 (March 2017): 326–29. http://dx.doi.org/10.1017/s1743921317009401.

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AbstractThe formation of molecules in the interstellar medium is significantly driven by grain chemistry, ranging from simple (e.g. H2) to relatively complex (e.g. CH3OH) products. The movement of atoms and molecules on amorphous ice surfaces is not well constrained, and this is a quintessential component of surface chemistry. We show that ice structure created by utilizing an off-lattice Monte Carlo kinetics model is highly dependent on deposition parameters (i.e. angle, rate, and temperature). The model, thus far, successfully predicts the densities of deposition rate- and temperature-dependent laboratory experiments. The simulations indicate, when angle and deposition rate increase, the density decreases. On the other hand, temperature has the opposite effect and will increase the density. We can make ices with desired densities and monitor how molecules, like CO, percolate through H2O ice pores. The strength of this model lies in the ability to replicate TPD-like experiments by monitoring molecules diffusing on and desorbing from user-defined surfaces.

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38

Galoyan, A. S., and V. V. Uzhinsky. "Glauber Monte Carlo program for NICA/MPD and CBM experiments." Physics of Particles and Nuclei Letters 12, no. 1 (January 2015): 166–69. http://dx.doi.org/10.1134/s1547477115010094.

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39

RUNOV, ALEXEY M., S. V. KASILOV, R. SCHNEIDER, and D. REITER. "3D Monte Carlo modelling of edge plasmas in fusion experiments." Journal of Plasma Physics 72, no. 06 (December 2006): 1109. http://dx.doi.org/10.1017/s0022377806005745.

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40

Meyer-Ortmanns, Hildegard. "Proposal of a new upgrading procedure for Monte Carlo experiments." Zeitschrift f�r Physik C Particles and Fields 27, no. 4 (December 1985): 553–58. http://dx.doi.org/10.1007/bf01436509.

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41

Wittmeyer, G., and S. P. Neuman. "Monte Carlo experiments with robust estimation of aquifer model parameters." Advances in Water Resources 14, no. 5 (October 1991): 252–72. http://dx.doi.org/10.1016/0309-1708(91)90038-p.

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42

Ghorbel, N., S. Fakhfakh, O. Jbara, S. Odof, S. Rondot, Z. Fakhfakh, and A. Kallel. "EPMA analysis of insulating materials: Monte Carlo simulations and experiments." Journal of Physics D: Applied Physics 38, no. 8 (April 2, 2005): 1239–47. http://dx.doi.org/10.1088/0022-3727/38/8/022.

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43

Bocchetti, V., and H. T. Diep. "Melting of rare-gas crystals: Monte Carlo simulation versus experiments." Journal of Chemical Physics 138, no. 10 (March 14, 2013): 104122. http://dx.doi.org/10.1063/1.4794916.

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44

STONE, MATTHEW T., PIETER J. IN 'T VELD, YING LU, and ISAAC C. SANCHEZ. "Hydrophobic/hydrophilic solvation: inferences from Monte Carlo simulations and experiments." Molecular Physics 100, no. 17 (September 10, 2002): 2773–92. http://dx.doi.org/10.1080/00268970210139912.

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45

Verberck, B., V. Heresanu, S. Rouzière, J. Cambedouzou, P. Launois, E. Kováts, S. Pekker, G. A. Vliegenthart, K. H. Michel, and G. Gompper. "Fullerene‐cubane: X‐ray Scattering Experiments and Monte Carlo Simulations." Fullerenes, Nanotubes and Carbon Nanostructures 16, no. 5-6 (September 2008): 293–300. http://dx.doi.org/10.1080/15363830802205830.

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46

Rapaport, D. C. "Monte Carlo experiments on percolation: the influence of boundary conditions." Journal of Physics A: Mathematical and General 18, no. 3 (February 21, 1985): L175—L179. http://dx.doi.org/10.1088/0305-4470/18/3/014.

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47

Kato, Takafumi. "A comparison of spatial error models through Monte Carlo experiments." Economic Modelling 30 (January 2013): 743–53. http://dx.doi.org/10.1016/j.econmod.2012.10.010.

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48

Hussein, Esam M. A. "Center-of-mass Monte Carlo simulation of neutron scattering experiments." International Journal of Radiation Applications and Instrumentation. Part A. Applied Radiation and Isotopes 41, no. 10-11 (January 1990): 1033–39. http://dx.doi.org/10.1016/0883-2889(90)90171-c.

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49

MILSTEIN, G. N., O. REIß, and J. SCHOENMAKERS. "A NEW MONTE CARLO METHOD FOR AMERICAN OPTIONS." International Journal of Theoretical and Applied Finance 07, no. 05 (August 2004): 591–614. http://dx.doi.org/10.1142/s0219024904002554.

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We introduce a new Monte Carlo method for constructing the exercise boundary of an American option in a generalized Black–Scholes framework. Based on a known exercise boundary, it is shown how to price and hedge the American option by Monte Carlo simulation of suitable probabilistic representations in connection with the respective parabolic boundary value problem. The method presented is supported by numerical experiments.

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50

Madhavrao, L. Rao, and Raj Rajagopalan. "Monte Carlo simulations for sintering of particle aggregates." Journal of Materials Research 4, no. 5 (October 1989): 1251–56. http://dx.doi.org/10.1557/jmr.1989.1251.

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A new Monte Carlo simulation procedure is developed for the initial stages of sintering of randomly packed particles. This simulation takes into account the possibility of crack initiation due to the stresses generated by the sintering particles and can accommodate both localized stresses and stress propagation. This procedure is used to investigate the sintering of two-dimensional aggregates of copper particles, and the results are compared with the results of model experiments available in the literature. The two-dimensional simulations presented here lead to shrinkage in area, decreases in perimeter, and particle rearrangements that are physically consistent with the expected behavior and experimental results. The proposed procedure can be extended to accommodate more complex features of sintering and to account for the material-dependent effects of stresses. It can also be used as a probe of sintering of bulk materials with random microstructure and to identify and design model experiments.

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