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1

Disordered alloys: Diffuse scattering and Monte Carlo simulations. Berlin: Springer Verlag, 1998.

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2

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

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3

Dimitrievski, Kristian. Monte Carlo simulations of supported biomembranes and protein folding. Göteborg: Göteborg University, Department of Physics, 2006.

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4

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

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5

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

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6

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

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7

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

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8

service), SpringerLink (Online, ed. An Introduction to Kinetic Monte Carlo Simulations of Surface Reactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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9

Jansen, A. P. J. An Introduction to Kinetic Monte Carlo Simulations of Surface Reactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29488-4.

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10

Gallis, Michael A. On the modeling of thermochemical non-equilibrium in particle simulations. London: Imperial College of Science, Technology & Medicine, Dept. of Aeronautics, 1995.

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11

Liu, J. Radiative interactions in chemically reacting compressible nozzle flows using Monte Carlo simulations. Norfolk, Va: Institute for Computational and Applied Mechanics, Old Dominion University, 1994.

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12

Uwe, Burghaus, ed. A practical guide to Monte Carlo simulations and classical molecular dynamics simulations by captain cook: An example booklet. Hauppauge, N.Y: Nova Science Publishers, 2005.

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13

Noriega-Muro, Antonio E. Nonstationarity and structural breaks in economic time series: Asymptotic theory and Monte Carlo simulations. Aldershot: Avebury, 1993.

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14

Markov chain Monte Carlo simulations and their statistical analysis: With web-based Fortran code. Hackensack, NJ: World Scientific, 2004.

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15

Bartsch, Ulrich. How much is enough?: Monte Carlo simulations of an oil stabilization fund for Nigeria. Washington, D.C: International Monetary Fund, African Dept., 2006.

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16

Markov chain Monte Carlo simulations and their statistical analysis: With web-based fortran code. Singapore: World Scientific Publishing, 2004.

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17

Hao, Ming-Hong. Statistical thermodynamics of protein folding: Comparison of a mean-field theory with Monte Carlo simulations. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1994.

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18

ʻAha Huliko ʻa Hawaiian Winter Workshop (9th 1997 University of Hawaii at Manoa). Monte Carlo simulations in oceanography: Proceedings, Aha Huliko a Hawaiian Winter Workshop, University of Hawaii at Manoa, January 14-17, 1997. Edited by Müller Peter 1944-, Henderson Diane, United States. Office of Naval Research., University of Hawaii at Manoa., and University of Hawaii at Manoa. School of Ocean and Earth Science and Technology. Honolulu, Hawaii: School of Ocean and Earth Science and Technology, 1997.

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19

D, Doll J., and Gubernatis J. E, eds. International Workshop on Quantum Simulations of Condensed Matter Phenomena, 8-11 August 1989, Los Alamos, NM, USA. Singapore: Teaneck, NJ, 1990.

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20

Curotto, Emanuele. Stochastic simulations of clusters: Quantum methods in flat and curved spaces. Boca Raton: Taylor & Francis, 2010.

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21

Stochastic simulations of clusters: Quantum methods in flat and curved spaces. Boca Raton: CRC Press, 2010.

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22

Schwenzfeger, K. J. Comparison of ERS-1 scatterometer Monte Carlo performance simulations using a weighted nonlinear least-squares and a maximum likelihood estimation method. Neubiberg: Hochschule der Bundeswehr München, 1985.

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23

Structure and thermodynamics of lattice polymers in bulk and at interfaces: A comparison of Ornstein-Zernike-like approaches to Monte Carlo simulations. Eindhoven: University of Eindhoven, 1996.

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24

Monte Carlo Simulations of Disordered Systems. World Scientific Pub Co Inc, 1992.

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25

Linn. Mc 93 International Conference, Monte Carlo Simulations. World Scientific Publishing Company, 1994.

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26

Chan, Wai Kin Victor, ed. Theory and Applications of Monte Carlo Simulations. InTech, 2013. http://dx.doi.org/10.5772/45892.

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27

Binder, Kurt, and David P. Landau. Guide to Monte Carlo Simulations in Statistical Physics. Cambridge University Press, 2014.

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28

Binder, Kurt, and David P. Landau. A Guide to Monte Carlo Simulations in Statistical Physics. Cambridge University Press, 2013.

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29

1944-, Binder K., ed. Monte Carlo and molecular dynamics simulations in polymer sciences. Oxford: Oxford University Press, 1995.

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30

A Guide to Monte Carlo Simulations in Statistical Physics. Cambridge University Press, 2005.

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31

1944-, Binder K., ed. Monte Carlo and molecular dynamics simulations in polymer science. New York: Oxford University Press, 1995.

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32

Kundu, Ashoke. Monte Carlo simulations of electrons and photons in matter. 1996.

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33

A Guide to Monte Carlo Simulations in Statistical Physics, Second Edition. 2nd ed. Cambridge University Press, 2005.

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34

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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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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35

Jansen, A. P. J. An Introduction to Kinetic Monte Carlo Simulations of Surface Reactions. Springer, 2012.

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36

Legrady, D. Improved Monte Carlo Methods with Application to Borehole Logging Simulations. IOS Press/Delft University Press, 2005.

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37

An Introduction To Kinetic Monte Carlo Simulations Of Surface Reactions. Springer, 2012.

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38

Volpi, Riccardo. Charge Transport Simulations for Organic Electronics: A Kinetic Monte Carlo Approach. Linköping University Electronic Press, 2016. http://dx.doi.org/10.3384/lic.diva-122991.

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39

Stephan, J., L. Vattuone, and J. M. Rogowska. A Practical Guide to Kinetic Monte Carlo Simulations And Classical Molecular Dynamics Simulations: An Example Book. Nova Science Publishers, 2006.

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40

Ydri, Badis. Computational Physics: An Introduction to Monte Carlo Simulations of Matrix Field Theory. World Scientific Publishing Co Pte Ltd, 2017.

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41

N, Tiwari S., and Langley Research Center, eds. Radiative interactions in chemically reacting compressible nozzle flows using Monte Carlo simulations. Norfolk, Va: Institute for Computational and Applied Mechanics, Old Dominion University, 1994.

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42

K, Dogra Virendra, Wilmoth Richard G, and Langley Research Center, eds. DSMC simulations of Mach 20 nitrogen flows about a 70ʻ́ blunted cone and its wake. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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43

Noriega-Muro, Antonio E. Nonstationarity and Structural Breaks in Economic Time Series: Asymptotic Theory and Monte Carlo Simulations. Ashgate Publishing, 1994.

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44

Berg, Bernd A. Markov Chain Monte Carlo Simulations And Their Statistical Analysis: With Web-based Fortran Code. World Scientific Publishing Company, 2004.

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45

Alberto, Mugnai, Smith Eric A, and United States. National Aeronautics and Space Administration., eds. Reverse Monte Carlo simulations of microwave radiative transfer in realistic 3-D rain clouds. [Washington, DC: National Aeronautics and Space Administration, 1994.

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46

Alberto, Mugnai, Smith Eric A, and United States. National Aeronautics and Space Administration., eds. Reverse Monte Carlo simulations of microwave radiative transfer in realistic 3-D rain clouds. [Washington, DC: National Aeronautics and Space Administration, 1994.

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47

Würl, Matthias. Towards Offline PET Monitoring at a Cyclotron-Based Proton Therapy Facility: Experiments and Monte Carlo Simulations. Springer Spektrum, 2016.

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48

N, Gupta Roop, Price Joseph M, and United States. National Aeronautics and Space Administration., eds. DSMC simulations of OREX entry conditions. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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49

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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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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50

Curotto, Emanuele. Stochastic Simulations of Clusters: Quantum Methods in Flat and Curved Spaces. Taylor & Francis Group, 2017.

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