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Books on the topic 'Metoda Monte Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Metod Monte-Karlo. 4th ed. Moskva: "Nauka," Glav. red. fiziko-matematicheskoĭ lit-ry, 1985.

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2

Lemieux, Christiane. Monte carlo and quasi-monte carlo sampling. New York: Springer, 2009.

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3

A, Whitlock Paula, and Wiley online library, eds. Monte Carlo methods. 2nd ed. Weinheim: Wiley-Blackwell, 2008.

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4

Kalos, Malvin H. Monte Carlo methods. New York: J. Wiley & Sons, 1986.

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5

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

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6

A, Whitlock Paula, ed. Monte Carlo methods. New York: J. Wiley & Sons, 1986.

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7

Monte Carlo: Concepts, algorithms, and applications. New York: Springer, 1996.

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8

Dunn, William L. Exploring Monte Carlo methods. Amsterdam: Elsevier/Academic Press, 2012.

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9

A primer for the Monte Carlo method. Boca Raton: CRC Press, 1994.

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10

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

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11

Franklin, Mendivil, ed. Explorations in Monte Carlo methods. Dordrecht: Springer, 2009.

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12

Mikhaĭlov, G. A. Vesovye metody Monte-Karlo. Novosibirsk: Izd-vo Sibirskogo otd-nii︠a︡ Rossiĭskoĭ akademii nauk, 2000.

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13

1944-, Niederreiter Harald, Talay D, and International Conference on Monte Carlo and Probabilistic Methods for Partial Differential Equations (2nd : 2004 : Juan-les-Pins, France), eds. Monte Carlo and Quasi-Monte Carlo methods 2004. Berlin: Springer, 2006.

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14

Sabelʹfelʹd, K. K. Metody Monte-Karlo v kraevykh zadachakh. Novosibirsk: "Nauka," Sibirskoe otd-nie, 1989.

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15

Nonparametric Monte Carlo tests and their applications. New York: Springer, 2005.

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16

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

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17

T, Barkema G., ed. Monte Carlo methods in statistical physics. Oxford: Clarendon Press, 1999.

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18

Monte Carlo strategies in scientific computing. New York: Springer, 2001.

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19

Mikhaĭlov, G. A. Ispolʹzovanie sopri︠a︡zhennykh uravneniĭ v metode Monte-Karlo. Novosibirsk: In-t vychislitelʹnoĭ matematiki i matematicheskoĭ geofiziki SO RAN, 2009.

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20

Mikhaĭlov, G. A. Ispolʹzovanie sopri︠a︡zhennykh uravneniĭ v metode Monte-Karlo. Novosibirsk: In-t vychislitelʹnoĭ matematiki i matematicheskoĭ geofiziki SO RAN, 2009.

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21

1968-, Keller Alexander, Heinrich Stefan, and Niederreiter Harald 1944-, eds. Monte Carlo and Quasi-Monte Carlo methods 2006. Berlin: Springer, 2008.

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22

Fishman, George S. Monte Carlo: Concepts, algorithms, and applications. 2nd ed. New York: Springer, 1997.

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23

Fox, Bennett L. Strategies for quasi-Monte Carlo. Boston: Kluwer Academic, 1999.

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24

Fishman, George S. Monte Carlo: Concepts, algorithms, and applications. New York: Springer-Verlag, 1995.

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25

Mengte Kaluo fang fa. Shanghai: Shanghai ke xue ji shu chu ban she, 1985.

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26

Monte Carlo methods for electromagnetics. Boca Raton, FL: Taylor & Francis Group, LLC, 2009.

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27

Random number generation and Monte Carlo methods. New York: Springer, 1998.

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28

Random number generation and Monte Carlo methods. 2nd ed. New York: Springer-Verlag, 2003.

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29

I, Lebedʹ V., and Losev N. F, eds. Metod Monte-Karlo v rentgenospektralʹnom mikroanalize. Novosibirsk: "Nauka," Sibirskoe otd-nie, 1989.

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30

Henryk, Woźniakowski, and SpringerLink (Online service), eds. Monte Carlo and Quasi-Monte Carlo Methods 2010. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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31

George, Casella, ed. Monte Carlo statistical methods. 2nd ed. New York: Springer, 2004.

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32

Casella, George, and Christian P. Robert. Monte Carlo Statistical Methods. 2nd ed. New York, USA: Springer, 2004.

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33

V, Voĭtishek A., and Akademii͡a nauk SSSR. Sibirskoe otdelenie. Vychislitelʹnyĭ t͡sentr., eds. VIII Vsesoi͡uznoe soveshchanie Metody Monte-Karlo v vychislitelʹnoĭ matematike i matematicheskoĭ fizike: Tezisy dokladov, 19-21 fevrali͡a 1991 goda. Novosibirsk: Akademii͡a nauk SSSR, Sibirskoe otd-nie, Vychislitelʹnyĭ t͡sentr, 1991.

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34

V, Bulavskiĭ I͡U, and Akademii͡a nauk SSSR. Sibirskoe otdelenie. Vychislitelʹnyĭ t͡sentr., eds. VII Vsesoi͡uznoe soveshchanie "Metody Monte-Karlo v vychislitelʹnoĭ matematike i matematicheskoĭ fizike": Tezisy dokladov 9-11 okti͡abri͡a 1985 g. Novosibirsk: Akademii͡a nauk SSSR, Sibirskoe otd-nie, Vychislitelʹnyĭ t͡sentr, 1985.

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35

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

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36

American Chemical Society. Division of Physical Chemistry, ed. Advances in quantum Monte Carlo. Washington, DC: American Chemical Society, 2012.

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37

Khisamutdinov, A. I. Algoritmy Monte-Karlo v i︠a︡dernoĭ geofizike. Novosibirsk: Nauka, 1985.

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38

N, Starikov V., Morozov A. A, and Filatov V. A, eds. Algoritmy Monte-Karlo v i͡a︡dernoĭ geofizike. Novosibirsk: Izd-vo "Nauka," Sibirskoe otd-nie, 1985.

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39

Ericson, Peter. A Monte Carlo evaluation of labor supply models. Göteborg, Sweden: Dept. of Economics, School of Economics and Commercial Law, 1996.

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40

International Conference on Monte Carlo Simulation (2000 Monte Carlo, Monaco). Monte Carlo simulation: Proceedings of the International Conference on Monte Carlo Simulation, Principality of Monaco, 18-21 June 2000. Lisse: A.A. Balkema, 2001.

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41

Borzdov, V. M. Modelirovanie metodom Monte-Karlo pribornykh struktur integralʹnoĭ ėlektroniki. Minsk: BGU, 2007.

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42

Simulation and Monte Carlo: With applications in finance and MCMC. Wiley, 2007.

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43

Simulation and Monte Carlo: With applications in finance and MCMC. Wiley, 2007.

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44

Dagpunar, J. S. Simulation and Monte Carlo: With Applications in Finance and MCMC. Wiley & Sons, Incorporated, John, 2007.

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45

Lemieux, Christiane. Monte Carlo and Quasi-Monte Carlo Sampling. Springer, 2010.

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46

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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47

Boudreau, Joseph F., and Eric S. Swanson. Monte Carlo methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0007.

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Monte Carlo methods are those designed to obtain numerical answers with the use of random numbers . This chapter discusses random engines, which provide a pseudo-random pattern of bits, and their use in for sampling a variety of nonuniform distributions, for both continuous and discrete variables. A wide selection of uniform and nonuniform variate generators from the C++ standard library are reviewed, and common techniques for generating custom nonuniform variates are discussed. The chapter presents the uses of Monte Carlo to evaluate integrals, particularly multidimensional integrals, and then introduces the important method of Markov chain Monte Carlo, suitable for solving a wide range of scientific problems that require the sampling of complicated multivariate distributions. Relevant topics in probability and statistics are also introduced in this chapter. Finally, the topics of thermalization, autocorrelation, multimodality, and Gibbs sampling are presented.

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48

A, Mikhaĭlov G., and Akademii͡a︡ nauk SSSR. Sibirskoe otdelenie. Vychislitelʹnyĭ t͡s︡entr., eds. Chislennye metody statisticheskogo modelirovanii͡a︡: Sbornik nauchnykh trudov. Novosibirsk: Akademii͡a︡ nauk SSSR, Sibirskoe otd-nie, Vychislitelʹnyĭ t͡s︡entr, 1987.

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49

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

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50

1957-, Madras Neal Noah, Fields Institute for Research in Mathematical Sciences., and Workshop on Monte Carlo Methods (1998 : Toronto, Ont.), eds. Monte Carlo methods. Providence, RI: American Mathematical Society, 2000.

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