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Books on the topic 'Overtime. Monte Carlo method. Simulation methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

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

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2

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

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3

George, Casella, and SpringerLink (Online service), eds. Introducing Monte Carlo Methods with R. New York, NY: Springer Science+Business Media, LLC, 2010.

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4

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

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5

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

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6

Biswas, A. Application of monte-carlo method in simulation of sputtered thin film surfaces. Mumbai: Bhabha Atomic Research Centre, 2005.

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7

Bufler, Fabian M. Full-band Monte Carlo simulation of nanoscale strained silicon MOSFETs. Konstanz: Hartung-Gorre, 2003.

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8

Koura, Katsuhisa. Comparison between the null-collision and time-counter direct-simulation Monte Carlo methods: leading-edge flow. Tokyo: National Aerospace Laboratory, 1989.

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9

Geostatistical simulation: Models and algorithms. New York: Springer, 2002.

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10

Monte Carlo simulation for the pharmaceutical industry: Concepts, algorithms, and case studies. Boca Raton: CRC Press, 2011.

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11

MacKeown, P. K. Stochastic simulation in physics. New York: Springer, 1997.

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12

Resampling: The new statistics. 2nd ed. Arlington, VA: Resampling Stats, 1999.

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13

Lantuéjoul, Christian. Geostatistical simulation: Models and algorithms. Berlin: Springer, 2002.

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14

Wenyuan, Li, ed. Reliability assessment of electric power systems using Monte Carlo methods. New York: Plenum Press, 1994.

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15

Singh, G. Venturer: Micro based appraisal of risk. (Leeds): SAR Investment Properties, 1985.

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16

Pyeon, Jae H. Improving transportation construction project performance: Development of a model to support the decision-making process for incentive/disincentive construction projects. San Jose, CA: Mineta Transportation Institute, College of Business, San José State University, 2010.

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17

International Conference on Computational Mathematics. The International Conference on Computational Mathematics: Proceedings. Novosibirsk: ICM&MG, 2002.

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18

Mitchell, Frederick J. Monte Carlo Simulation: Methods, Assessment and Applications. Nova Science Publishers, Incorporated, 2017.

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19

Rubino, Gerardo, and Bruno Tuffin. Rare Event Simulation Using Monte Carlo Methods. Wiley & Sons, Incorporated, John, 2009.

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20

Thomopoulos, Nick T. Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models. Springer, 2012.

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21

Thomopoulos, Nick T. T. Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models. Springer, 2015.

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22

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

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23

1949-, Morin Richard L., ed. Monte Carlo simulation in the radiological sciences. Boca Raton, Fla: CRC Press, 1988.

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24

Carsey, Thomas M., and Jeffrey J. (Joseph) Harden. Monte Carlo Simulation and Resampling Methods for Social Science. SAGE Publications, Incorporated, 2013.

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25

Grazia, Maria. Monte Carlo Simulation for Experimental Physics: A Practical Introduction to Concepts, Methods, Technology and Tools. Taylor & Francis Group, 2019.

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26

Allen, Michael P., and Dominic J. Tildesley. Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0004.

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The estimation of integrals by Monte Carlo sampling is introduced through a simple example. The chapter then explains importance sampling, and the use of the Metropolis and Barker forms of the transition matrix defined in terms of the underlying matrix of the Markov chain. The creation of an appropriately weighted set of states in the canonical ensemble is described in detail and the method is extended to the isothermal–isobaric, grand canonical and semi-grand ensembles. The Monte Carlo simulation of molecular fluids and fluids containing flexible molecules using a reptation algorithm is discussed. The parallel tempering or replica exchange method for more efficient exploration of the phase space is introduced, and recent advances including solute tempering and convective replica exchange algorithms are described.
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27

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

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28

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

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29

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

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30

1944-, Binder K., ed. Monte Carlo methods in statistical physics. 2nd ed. Berlin: Springer-Verlag, 1986.

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31

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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32

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

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33

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

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34

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

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35

(Editor), Andreas Kling, Fernando J.C. Barao (Editor), Masayuki Nakagawa (Editor), Luis Tavora (Editor), and Pedro Vaz (Editor), eds. Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications. Springer, 2001.

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36

Korn, Granino A. Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies. Wiley & Sons, Incorporated, John, 2013.

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37

Korn, Granino A. Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies. Wiley & Sons, Incorporated, John, 2013.

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38

Korn, Granino A. Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies. Wiley, 2013.

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39

Korn, Granino A. Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies. Wiley & Sons, Incorporated, John, 2013.

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40

Chan-Hong, Chung, and United States. National Aeronautics and Space Administration., eds. Simulation of low-density nozzle plumes in non-zero ambient pressures. [Washington, D.C.]: NASA, 1994.

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41

Giovanni, Ciccotti, Frenkel Daan 1948-, and McDonald Ian R, eds. Simulation of liquids and solids: Molecular dynamics and Monte Carlo methods in statistical mechanics. Amsterdam: North-Holland, 1987.

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42

Simon, Julian Lincoln. Resampling: The new statistics. Resampling Stats, 1995.

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43

United States. National Aeronautics and Space Administration., ed. RENEW v3.2 user's manual, maintenance estimation simulation for Space Station Freedom. [Washington, DC]: National Aeronautics and Space Administration, 1993.

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44

RENEW v3.2 user's manual, maintenance estimation simulation for Space Station Freedom. [Washington, DC]: National Aeronautics and Space Administration, 1993.

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45

United States. National Aeronautics and Space Administration., ed. RENEW v3.2 user's manual, maintenance estimation simulation for Space Station Freedom. [Washington, DC]: National Aeronautics and Space Administration, 1993.

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46

United States. National Aeronautics and Space Administration., ed. RENEW v3.2 user's manual, maintenance estimation simulation for Space Station Freedom. [Washington, DC]: National Aeronautics and Space Administration, 1993.

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47

Rubin, Donald, Xiaoqin Wang, Li Yin, and Elizabeth Zell. Bayesian causal inference: Approaches to estimating the effect of treating hospital type on cancer survival in Sweden using principal stratification. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.24.

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This article discusses the use of Bayesian causal inference, and more specifically the posterior predictive approach of Rubin’s causal model (RCM) and methods of principal stratification, in estimating the effects of ‘treating hospital type’ on cancer survival. Using the Karolinska Institute in Stockholm, Sweden, as a case study, the article investigates which type of hospital (large patient volume vs. small volume) is superior for treating certain serious conditions. The study examines which factors may reasonably be considered ignorable in the context of covariates available, as well as non-compliance complications due to transfers between hospital types for treatment. The article first provides an overview of the general Bayesian approach to causal inference, primarily with ignorable treatment assignment, before introducing the proposed approach and motivating it using simple method-of-moments summary statistics. Finally, the results of simulation using Markov chain Monte Carlo (MCMC) methods are presented.
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