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Libri sul tema "Monte Carlo Simulation Technique"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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1

Mun, Johnathan. Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. 2nd ed. New York: Wiley, 2010.

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2

A, Viterna Larry, and Lewis Research Center, eds. ETARA PC version 3.3 user's guide: Reliability, availability, maintainability simulation model. Cleveland, Ohio: NASA Lewis Research Center, 1991.

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3

A, Viterna Larry, and Lewis Research Center, eds. ETARA PC version 3.3 user's guide: Reliability, availability, maintainability simulation model. Cleveland, Ohio: NASA Lewis Research Center, 1991.

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4

A, Viterna Larry, and Lewis Research Center, eds. ETARA PC version 3.3 user's guide: Reliability, availability, maintainability simulation model. Cleveland, Ohio: NASA Lewis Research Center, 1991.

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5

Mooney, Christopher. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1997. http://dx.doi.org/10.4135/9781412985116.

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6

Zhu, Zhen, and Hari Rajagopalan. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908969.

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7

I, Schueller G., ed. Monte Carlo simulation. Lisse: A.A. Balkema, 2001.

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8

Hess, Karl, ed. Monte Carlo Device Simulation. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7.

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9

Thomopoulos, Nick T. Essentials of Monte Carlo Simulation. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6022-0.

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10

Brandimarte, Paolo. Handbook in Monte Carlo Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593264.

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11

Gleißner, Werner, and Marco Wolfrum. Risikoaggregation und Monte-Carlo-Simulation. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24274-9.

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12

Kroese, Dirk P., Thomas Taimre, Zdravko I. Botev, and Rueven Y. Rubinstein. Simulation and the Monte Carlo Method. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470285312.

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13

Binder, Kurt, and Dieter W. Heermann. Monte Carlo Simulation in Statistical Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-03163-2.

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14

Chen, Ding-Geng, and John Dean Chen, eds. Monte-Carlo Simulation-Based Statistical Modeling. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0.

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15

Binder, Kurt, and Dieter W. Heermann. Monte Carlo Simulation in Statistical Physics. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10758-1.

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16

Moglestue, C. Monte Carlo Simulation of Semiconductor Devices. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8133-2.

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17

Graham, Carl, and Denis Talay. Stochastic Simulation and Monte Carlo Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39363-1.

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18

Rubinstein, Reuven Y., and Dirk P. Kroese. Simulation and the Monte Carlo Method. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118631980.

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19

Binder, Kurt, and Dieter W. Heermann. Monte Carlo Simulation in Statistical Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-08854-8.

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20

Binder, Kurt, and Dieter W. Heermann. Monte Carlo Simulation in Statistical Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04685-2.

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21

Binder, Kurt, and Dieter W. Heermann. Monte Carlo Simulation in Statistical Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-30273-6.

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22

Binder, Kurt, and Dieter W. Heermann. Monte Carlo Simulation in Statistical Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03336-4.

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23

Moglestue, C. Monte Carlo simulation of semiconductor devices. London: Chapman & Hall, 1993.

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24

A, Young Jennifer, and Langley Research Center, eds. Monte Carlo simulation of endlinking oligomers. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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25

Moglestue, C. Monte Carlo Simulation of Semiconductor Devices. Dordrecht: Springer Netherlands, 1993.

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26

P, Kroese Dirk, ed. Simulation and the monte carlo method. 2nd ed. Hoboken, N.J: John Wiley & Sons, 2008.

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27

Pierre, L' Ecuyer, and Owen Art B, eds. Monte Carlo and quasi-Monte Carlo methods 2008. Heidelberg: Springer, 2009.

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28

Lim, Chjan, and Joseph Nebus, eds. Vorticity, Statistical Mechanics, and Monte Carlo Simulation. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49431-9.

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29

Rubino, Gerardo, and Bruno Tuffin, eds. Rare Event Simulation using Monte Carlo Methods. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745403.

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30

1955-, Rubino Gerardo, and Tuffin Bruno, eds. Rare event simulation using Monte Carlo methods. Hoboken, N.J: Wiley, 2009.

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31

Bernd, Meinerzhagen, ed. Hierarchical device simulation: The Monte-Carlo perspective. Wien: Springer, 2003.

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32

Wang, Hui. Monte Carlo simulation with applications to finance. Boca Raton: CRC Press, 2012.

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33

Allen, Michael P., and Dominic J. Tildesley. Advanced Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0009.

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Abstract (sommario):

This chapter describes the ways in which the Monte Carlo importance sampling method may be adapted to improve the calculation of ensemble averages, particularly those associated with free energy differences. These approaches include umbrella sampling, non-Boltzmann sampling, the Wang–Landau method, and nested sampling. In addition, a range of special techniques have been developed to accelerate the simulation of flexible molecules, such as polymers. These approaches are illustrated with scientific examples and program code. The chapter also explains the analysis of such simulations using techniques such as weighted histograms, and acceptance ratio calculations. Practical advice on selection of methods, parameters, and the direction in which to make comparisons, are given. Monte Carlo methods for modelling phase equilibria and chemical reactions at equilibrium are described.

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34

Hastings, Frank Desmond. A Monte Carlo FDTD technique for rough surface scattering. 1993.

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35

Korn, Granino A. Advanced Dynamic-System Simulation: Model-Replication Techniques and Monte Carlo Simulation. Wiley & Sons, Incorporated, John, 2007.

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36

Korn, Granino A. Advanced Dynamic-System Simulation: Model-Replication Techniques and Monte Carlo Simulation. Wiley & Sons, Incorporated, John, 2010.

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37

Korn, Granino A. Advanced Dynamic-System Simulation: Model-Replication Techniques and Monte Carlo Simulation. Wiley & Sons, Incorporated, John, 2006.

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38

Advanced Dynamic-system Simulation: Model-replication Techniques and Monte Carlo Simulation. Wiley-Interscience, 2007.

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39

Medin, Joakim. Studies of clinical proton dosimetry using Monte Carlo simulation and experimental techniques. 1997.

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40

Value-Focused Thinking in the Presence of Weight Ambiguity: A Solution Technique Using Monte Carlo Simulation. Storming Media, 2004.

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41

Boudreau, Joseph F., and Eric S. Swanson. Simulation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0015.

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Abstract (sommario):

This chapter is devoted to Monte Carlo simulation of stochastic processes, both fundamental processes and those involving radiation transport through macroscopic material. The computation of fundamental processes builds on the treatment of rotations and Lorentz transformations from the previous chapter and expands it with a discussion of computational techniques for the evaluation of Feynman diagrams. The simulation of radiation transport covers electromagnetic processes such as ionization energy loss, bremsstrahlung, and pair production. A discussion of real-life challenges in the simulation of radiation transport is included, as well as a brief discussion of simulation toolkits that are available for solving industrial-strength problems. The discussion is intended to give an overview of some of the principal computational and numerical techniques enabling these toolkits.

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42

Mun, Johnathan. Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting, and Optimization Techniques. Wiley & Sons, Incorporated, John, 2008.

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43

Mun, Johnathan. Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting, and Optimization Techniques. Wiley & Sons, Incorporated, John, 2006.

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44

Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. Hoboken, NJ: John Wiley & Sons, 2006.

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45

Allen, Michael P., and Dominic J. Tildesley. Computer Simulation of Liquids. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.001.0001.

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Abstract (sommario):

This book provides a practical guide to molecular dynamics and Monte Carlo simulation techniques used in the modelling of simple and complex liquids. Computer simulation is an essential tool in studying the chemistry and physics of condensed matter, complementing and reinforcing both experiment and theory. Simulations provide detailed information about structure and dynamics, essential to understand the many fluid systems that play a key role in our daily lives: polymers, gels, colloidal suspensions, liquid crystals, biological membranes, and glasses. The second edition of this pioneering book aims to explain how simulation programs work, how to use them, and how to interpret the results, with examples of the latest research in this rapidly evolving field. Accompanying programs in Fortran and Python provide practical, hands-on, illustrations of the ideas in the text.

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46

Tabatabaian, Zinat. Fast neutron transmission and tomography simulation using Monte Carlo techniques for the examination of large industrial biological objects. 1997.

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47

Allen, Michael P., and Dominic J. Tildesley. Quantum simulations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0013.

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Abstract (sommario):

This chapter covers the introduction of quantum mechanics into computer simulation methods. The chapter begins by explaining how electronic degrees of freedom may be handled in an ab initio fashion and how the resulting forces are included in the classical dynamics of the nuclei. The technique for combining the ab initio molecular dynamics of a small region, with classical dynamics or molecular mechanics applied to the surrounding environment, is explained. There is a section on handling quantum degrees of freedom, such as low-mass nuclei, by discretized path integral methods, complete with practical code examples. The problem of calculating quantum time correlation functions is addressed. Ground-state quantum Monte Carlo methods are explained, and the chapter concludes with a forward look to the future development of such techniques particularly to systems that include excited electronic states.

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48

Monte Carlo simulation. Thousand Oaks, Calif: Sage Publications, 1997.

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49

Monte Carlo Simulation. Taylor & Francis, 2001.

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50

Schneider, Gino. Die Monte Carlo Simulation. GRIN Verlag GmbH, 2013.

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