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Relevant bibliographies by topics / Statistical simulations

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Academic literature on the topic 'Statistical simulations'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Statistical simulations"

1

Bell, J. F., and Mark E. Johnson. "Multivariate Statistical Simulations." Statistician 37, no. 1 (1988): 89. http://dx.doi.org/10.2307/2348394.

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Gore, Ross, Paul F. Reynolds Jr., David Kamensky, Saikou Diallo, and Jose Padilla. "Statistical Debugging for Simulations." ACM Transactions on Modeling and Computer Simulation 25, no. 3 (2015): 1–26. http://dx.doi.org/10.1145/2699722.

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Boulesteix, Anne-Laure, Rolf HH Groenwold, Michal Abrahamowicz, et al. "Introduction to statistical simulations in health research." BMJ Open 10, no. 12 (2020): e039921. http://dx.doi.org/10.1136/bmjopen-2020-039921.

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In health research, statistical methods are frequently used to address a wide variety of research questions. For almost every analytical challenge, different methods are available. But how do we choose between different methods and how do we judge whether the chosen method is appropriate for our specific study? Like in any science, in statistics, experiments can be run to find out which methods should be used under which circumstances. The main objective of this paper is to demonstrate that simulation studies, that is, experiments investigating synthetic data with known properties, are an inva

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Batalo, B., LS Souza, and K. Yamazaki. "Statistical moments for simulation calibration with model-bridge." Journal of Physics: Conference Series 2701, no. 1 (2024): 012047. http://dx.doi.org/10.1088/1742-6596/2701/1/012047.

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Abstract Computer simulations are actively used for analyzing complex phenomena, especially in fields where access to their real-world counterparts is not feasible, such as physics, chemistry, material science, and others. The key to executing successful simulations is making sure that the parameters of a simulator reflect real-world scenarios, a tedious and error-prone effort addressed through simulation calibration. Recently, several methods have been proposed to automatize this task by learning from previously calibrated simulations using a model-bridge paradigm: a complex simulation is rep

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Orlov, A. I. "STATISTICAL SIMULATIONS METHOD IN APPLIED STATISTICS." Industrial laboratory. Diagnostics of materials 85, no. 5 (2019): 67–79. http://dx.doi.org/10.26896/1028-6861-2019-85-5-67-79.

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The new paradigm of mathematical research methods is based on the effective application of information and communication technologies both in calculating the characteristics of the methods of data analysis and in simulation modeling. Pseudo-random number generators underlie many modern data analysis technologies. To solve specific applied problems, researchers permanently develop the new methods for processing statistical data, i.e., measurement results (observations, tests, analyzes, experiments) and expert estimations. The properties of each newly proposed method must be studied. The intelle

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Strayer, Jeremy, and Amber Matuszewski. "Statistical Literacy: Simulations with Dolphins." Mathematics Teacher 109, no. 8 (2016): 606–11. http://dx.doi.org/10.5951/mathteacher.109.8.0606.

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Ševčíková, Hana. "Statistical Simulations on Parallel Computers." Journal of Computational and Graphical Statistics 13, no. 4 (2004): 886–906. http://dx.doi.org/10.1198/106186004x12605.

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Gould, Harvey, Lynna Spomick, Jan Tobochnik, Beate Schmittmann, Susan R. McKay, and Wolfgang Christian. "Thermal and Statistical Physics Simulations." Computers in Physics 10, no. 3 (1996): 258. http://dx.doi.org/10.1063/1.4822396.

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Campbell, Katherine. "Statistical calibration of computer simulations." Reliability Engineering & System Safety 91, no. 10-11 (2006): 1358–63. http://dx.doi.org/10.1016/j.ress.2005.11.032.

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Rögnvaldsson, Ólafur, Philippe Crochet, and Haraldur Ólafsson. "Mapping of precipitation in Iceland using numerical simulations and statistical modeling." Meteorologische Zeitschrift 13, no. 3 (2004): 209–19. http://dx.doi.org/10.1127/0941-2948/2004/0013-0209.

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Dissertations / Theses on the topic "Statistical simulations"

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Atkinson, Richard Alan. "Statistical analysis of molecular dynamic simulations." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305971.

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Herd, Benjamin. "Statistical runtime verification of agent-based simulations." Thesis, King's College London (University of London), 2015. http://kclpure.kcl.ac.uk/portal/en/theses/statistical-runtime-verification-of-agentbased-simulations(ae71609a-c4a5-4bde-a000-3fe4e6b9759d).html.

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As a consequence of the growing adoption of agent-based simulations as decision making tools in various (potentially also critical) areas, questions of veracity and validity become increasingly important. In general software and hardware development, formal verification – particularly model checking – has been applied successfully to a wide range of problems; due to their immense complexity, however, agent-based simulations lend themselves to conventional formal verification only in very simple cases and at a disproportionately high cost. The purpose of this work is to address this problem and

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Kamsani, Noor 'Ain. "Statistical circuit simulations - from ‘atomistic’ compact models to statistical standard cell characterisation." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2720/.

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This thesis describes the development and application of statistical circuit simulation methodologies to analyse digital circuits subject to intrinsic parameter fluctuations. The specific nature of intrinsic parameter fluctuations are discussed, and we explain the crucial importance to the semiconductor industry of developing design tools which accurately account for their effects. Current work in the area is reviewed, and three important factors are made clear: any statistical circuit simulation methodology must be based on physically correct, predictive models of device variability; the stat

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VanderNoot, Theodore John Carleton University Dissertation Chemistry. "Two dimensional electrocrystallization; simulations, statistical analysis and anodic monolayer formation." Ottawa, 1985.

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McMillan, David G. "Statistical analyses of geomagnetic dipole variations, reversals and geodynamo simulations /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3090447.

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Amir-Azizi, Siamak. "Linear filtering algorithms for Monte Carlo simulations." Thesis, University of Southampton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280859.

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Galassi, Giulio Roberto. "Monte Carlo simulations of diatomics, polymers and alkanes." Thesis, University of Southampton, 1993. https://eprints.soton.ac.uk/421719/.

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Trefz, Benjamin [Verfasser]. "Computer simulations of the statistical behaviour of active particles / Benjamin Trefz." Mainz : Universitätsbibliothek Mainz, 2016. http://d-nb.info/1119480183/34.

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Rodgers, Jocelyn Michelle. "Statistical mechanical theory for and simulations of charged fluids and water." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8561.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Chemical Physics Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Savino, Mary Edith. "Statistical learning methods for nonlinear geochemical problems." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM032.

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Dans le cadre de simulations numériques de systèmes géochimiques s'intégrant dans un projet de stockage profond de déchets hautement radioactifs, nous proposons dans cette thèse deux méthodes d'estimation de fonction ainsi qu'une méthode de sélection de variables dans un modèle de régression non-paramétrique multivarié.Plus précisément, dans le Chapitre 2, nous présentons une procédure d'apprentissage actif utilisant les processus Gaussiens pour approcher des fonctions inconnues ayant plusieurs variables d'entrée. Cette méthode permet à chaque itération le calcul de l'incertitude globale sur l

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Books on the topic "Statistical simulations"

1

Lynna, Spornick, Tobochnik Jan 1953-, and Consortium for Upper-Level Physics Software., eds. Thermal and statistical physics simulations. Wiley, 1995.

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Kottemann Ph.D, Jeffrey E. Illuminating Statistical Analysis Using Scenarios and Simulations. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119296386.

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Armstrong, Margaret. Plurigaussian Simulations in Geosciences. Springer-Verlag Berlin Heidelberg, 2011.

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Geostatistical, Simulation Workshop (1993 Fontainebleau France). Geostatistical simulations: Proceedings of the Geostatistical Simulation Workshop, Fontainebleau, France, 27-28 May 1993. Kluwer Academic Publishers, 1994.

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5

Landau, David P. A guide to Monte Carlo simulations in statistical physics. 3rd ed. Cambridge University Press, 2009.

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Kamberaj, Hiqmet. Molecular Dynamics Simulations in Statistical Physics: Theory and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35702-3.

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1944-, Binder K., ed. A guide to Monte Carlo simulations in statistical physics. Cambridge University Press, 2000.

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Landau, David P. A guide to Monte Carlo simulations in statistical physics. 2nd ed. Cambridge University Press, 2005.

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Landau, David P. A guide to Monte Carlo simulations in statistical physics. 3rd ed. Cambridge University Press, 2009.

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1944-, Binder K., ed. A guide to Monte Carlo simulations in statistical physics. 3rd ed. Cambridge University Press, 2009.

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Book chapters on the topic "Statistical simulations"

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Binder, K. "Classical Statistical Mechanics." In Computer Simulations of Surfaces and Interfaces. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0173-1_1.

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Chudnovsky, A., and B. Kunin. "Statistical Fracture Mechanics." In Microscopic Simulations of Complex Hydrodynamic Phenomena. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4899-2314-1_25.

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Mitra, Subhankur, and Samrath Lal Chaplot. "Applications of Molecular Dynamics Simulations." In Computational Statistical Physics. Hindustan Book Agency, 2011. http://dx.doi.org/10.1007/978-93-86279-50-7_7.

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Grassberger, Peter, and Walter Nadler. "‘Go with the Winners’ Simulations." In Computational Statistical Physics. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04804-7_11.

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Cyganowski, Sasha, Peter Kloeden, and Jerzy Ombach. "Numerical Simulations and Statistical Inference." In Universitext. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56144-3_6.

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Kouh, Minjoon, and Taejoon Kouh. "Premise of Statistical Mechanics." In Thermal Physics Tutorials with Python Simulations. CRC Press, 2023. http://dx.doi.org/10.1201/9781003287841-7.

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Schinazi, Rinaldo B. "Simulations of Discrete Random Variables." In Probability with Statistical Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93635-8_6.

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Español, Pep. "Statistical Mechanics of Coarse-Graining." In Novel Methods in Soft Matter Simulations. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39895-0_3.

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Stauffer, D. "Monte Carlo Simulations in Statistical Physics." In Computer Simulation and Computer Algebra. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-97091-7_2.

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Stauffer, D. "Monte Carlo Simulations in Statistical Physics." In Computer Simulation and Computer Algebra. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-97174-7_2.

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Conference papers on the topic "Statistical simulations"

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Giachello, Martina, Giacomo Gradenigo, and Francesco Scardino. "Symplectic Quantization and Minkowskian Statistical Mechanics: simulations on a 1+1 lattice." In The 41st International Symposium on Lattice Field Theory. Sissa Medialab, 2024. https://doi.org/10.22323/1.466.0359.

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Nishimori, Hidetoshi. "Simulations of dynamical aspects of a neural network." In Computer-aided statistical physics. AIP, 1992. http://dx.doi.org/10.1063/1.41944.

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Heidelberger, Philip. "Statistical analysis of parallel simulations." In the 18th conference. ACM Press, 1986. http://dx.doi.org/10.1145/318242.318448.

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Blejec, Andrej. "Teaching statistics by using simulations on the Internet." In Statistics and the Internet. International Association for Statistical Education, 2003. http://dx.doi.org/10.52041/srap.03317.

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Simple probabilistic simulations, such as flipping a coin or rolling a dice, are used for illustration of statistical concepts and stochastic properties of modeled processes. With availability of personal computers in past decade, computer supported statistical simulations were introduced to statistics teaching. Such simulations - usually combined with graphic presentation of results and simulation itself - can be used for more obvious demonstration of statistical phenomena. With the advent of the Internet, new dimensions were introduced to teaching. The simulation software is easily available

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Whitaker, Nathaniel. "STATISTICAL THEORIES AND DIRECT NUMERICAL SIMULATIONS." In Proceedings of the Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304245_0003.

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Nielaba, P. "Rotator impurity in a crystal, lattice deformations and quantum Monte Carlo simulations." In Computer-aided statistical physics. AIP, 1992. http://dx.doi.org/10.1063/1.41935.

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Blejec, Andrej. "Presentation of statistical concepts with dynamic graphics and simulations in R." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17205.

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Understanding statistical concepts is important for proper use of statistics. The idea of using simulations and dynamic graphics to foster understanding of statistical concepts is not new. In recent years, R became the lingua franca for statistical data analysis. While R graphical devices are not meant for display of animated graphics, my aim is to use base R graphics for display of animated graphical sequences. To enable dynamic graphics in R, I developed a package animatoR, which supports smooth transitions of graphical elements and simplifies preparation of animated displays. I will show so

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Krasikov, M., V. Nelayev, V. Syakerckii, and V. Stempitsky. "End-to-end statistical process/device/circuit/system design." In Nano-Design, Technology, Computer Simulations, edited by Alexander I. Melker and Vladislav V. Nelayev. SPIE, 2008. http://dx.doi.org/10.1117/12.837070.

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Huang, Ya-Lin, Richard Fujimoto, Wonho Suh, Michael Hunter, and Christos Alexopoulos. "Statistical issues in ad hoc distributed simulations." In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6147790.

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Tokuyama, M., and I. Oppenheim. "Statistical Physics: Experiments, Theories and Computer Simulations." In 2nd Tohwa University International Meeting. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814528870.

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Reports on the topic "Statistical simulations"

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Ding, Yan, Sung-Chan Kim, Rusty L. Permenter, Richard B. Styles, and Jeffery A. Gebert. Simulations of Shoreline Changes along the Delaware Coast. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/39559.

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This technical report presents two applications of the GenCade model to simulate long-term shoreline evolution along the Delaware Coast driven by waves, inlet sediment transport, and longshore sediment transport. The simulations also include coastal protection practices such as periodic beach fills, post-storm nourishment, and sand bypassing. Two site-specific GenCade models were developed: one is for the coasts adjacent to the Indian River Inlet (IRI) and another is for Fenwick Island. In the first model, the sediment exchanges among the shoals and bars of the inlet were simulated by the Inle

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Prabhat, Mr, Dmitry Zubarev, and Jr ,. William A. Lester. Statistical Exploration of Electronic Structure of Molecules from Quantum Monte-Carlo Simulations. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1016359.

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Kleijnen, J. P. C., and J. C. Helton. Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/5894.

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Bednall, Timothy. Monte Carlo Simulations: Power Analysis and Beyond. Instats Inc., 2023. http://dx.doi.org/10.61700/gpienbvutyd38469.

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This 3-day workshop offers a comprehensive understanding of Monte Carlo simulations and power analysis using Mplus and R software. Tailored for PhD students, professors, and professional researchers across various disciplines, the workshop covers a range of topics from introduction to Monte Carlo simulations, principles of power analysis, to ethical considerations and future directions in the use of these techniques, equipping participants with practical skills and deep understanding of these advanced statistical tools.

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Glimm, James, Yunha Lee, Kenny Q. Ye, and David H. Sharp. Prediction Using Numerical Simulations, A Bayesian Framework for Uncertainty Quantification and its Statistical Challenge. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada417842.

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Bellan, Josette. A New Paradigm in Modeling and Simulations of Complex Oxidation Chemistry Using a Statistical Approach. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada500388.

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Maggioni, Mauro, Ioannis Kevrekidis, and Fei Lu. Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1960718.

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Frank, M., and J. Wolford. Application of Neutron Correlation Techniques to Warhead Authentication: Feasibility and Conceptual Requirements?Monte Carlo Simulations and Statistical Analysis. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/15014600.

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Allen, Luke, Robert Haehnel, and Yonghu Wenren. South Pole Station snowdrift model. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44943.

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The elevated building at Scott-Amundsen South Pole Station was designed to mitigate the effects of windblown snow on it and the surrounding infrastructure. Because the elevation of the snow surface increases annually, the station is periodically lifted on its support columns to maintain its design height above the snow surface. To assist with planning these lifts, this effort developed a computational model to simulate snowdrift formation around the elevated building. The model uses computational fluid dynamics methods and synthetic wind record generation derived from statistical analysis of m

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Helton, Jon C., and Maichael J. Shortencarier. A FORTRAN 77 Program and User's Guide for the Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/9722.

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