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Books on the topic 'Approche Monte-Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Niemi, Auli. Modeling flow in fractured medium: Uncertainty analysis with stochastic continuum approach. Technical Research Centre of Finland, 1994.

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2

Cowles, Mary Kathryn. A simulation approach to convergence rates for Markov chain Monte Carlo algorithms. University of Toronto, Dept. of Statistics, 1996.

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3

Rennison, Andrew. Comparing alternative output-gap estimators: A Monte Carlo approach. Bank of Canada, 2003.

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4

K, Fraley S., ed. A Monte Carlo primer: A practical approach to radiation transport. Kluwer Academic/Plenum, 2002.

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5

Workshop on Monte Carlo Approach to Biopolymers and Protein Folding (1997 Jülich, Germany). Workshop on Monte Carlo Approach to Biopolymers and Protein Folding, HLRZ, Forschungszentrum Jülich, Germany, 3-5 December 1997. World Scientific, 1998.

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6

Mehta, Shailesh. The Monte Carlo approach to calculating radial distribution functions in dense plasmas. University of Birmingham, 1995.

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7

Egginton, D. M. A Monte Carlo study of alternative approaches to balancing the national accounts. Economics Division, Bank of England, 1990.

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8

Racine, J. S. The semiparametric approach to the estimation of systems of equations models in the presence of heteroskedasticity of unknown form. Dept. of Economics, York University, 1989.

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9

Kim, Chang-Jin. Sources of monetary growth uncertainty: An encompassing approach based on Monte Carlo method. Dept. of Economics, York University, 1989.

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10

Gallis, M. A. The maximum entropy approach applied to energy exchange, chemical reactions and ionisation in the direct simulation Monte Carlo method for rarefied hypersonic flows. Imperial College of Science Technology and Medicine, 1993.

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11

Workshop on Monte Carlo Approach to Biopolymers and Protein Folding (1997 Höchstleistungsrechenzentrum). Workshop on Monte Carlo Approach to Biopolymers and Protein Folding, HLRZ, Forschungszentrum Jülich, Germany, 3-5 December 1997. World Scientific, 1998.

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12

Rubenstein, Reuven Y. The cross entropy method: A unified approach to combinatorial optimization, Monte-Carlo simluation, and machine learning. Springer, 2004.

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13

Zürich, Eidgenössische Technische Hochschule, ed. Static inductance extraction by means of the Monte Carlo method and comparison with related approaches. Hartung-Gorre, 2001.

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14

Dufour, Jean-Marie. Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics. Centre interuniversitaire de recherche en économie quantitative, 2005.

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15

Rubinstein, Reuven Y. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer New York, 2004.

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16

Górski, Jarosław. Non-linear models of structures with random geometric and material imperfactions [sic] simulation-based approach. Wydawn. Politechniki Gdańskiej, 2006.

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17

Workshop, Collegium Internationale Neuro-psychopharmacologicum. Current therapeutic approaches to panic and other anxiety disorders: Collegium Internationale Neuro-psychopharmacologicum, Regional Workshop, Monte Carlo, November 20-22, 1993. Edited by Darcourt Guy. Karger, 1994.

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18

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

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19

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 techn

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20

Becca, Federico, and Sandro Sorella. Quantum Monte Carlo Approaches for Correlated Systems. Cambridge University Press, 2017.

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21

Martin, Andrew D. Bayesian Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0021.

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This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian infer

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22

N, Varma Matesh, Chatterjee Aloke, and DOE Workshop on Computational Approaches in Molecular Radiation Biology: Monte Carlo Methods (1993 : Irvine, Calif.), eds. Computational approaches in molecular radiation biology: Monte Carlo methods. Plenum Press, 1994.

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23

Dupree, Stephen A., and Stanley K. Fraley. A Monte Carlo Primer: A Practical Approach to Radiation Transport. Springer, 2012.

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24

Workshops on Monte Carlo Approach to Biopolymers and Protein Folding. World Scientific Publishing Company, 1998.

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25

Dupree, Stephen A., and Stanley K. Fraley. A Monte Carlo Primer: A Practical Approach to Radiation Transport. Springer, 2001.

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26

Ranawake, Udaya A. Cluster partitioning approaches to parallel Monte Carlo simulation on multiprocessors. 1992.

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27

A Monte Carlo Primer: A Practical Approach to Radiation Transport. Springer US, 2002.

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28

Dupree, Stephen A., and Stanley K. Fraley. A Monte Carlo Primer: A Practical Approach to Radiation Transport. Springer, 2012.

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29

Martin, Richard M., Lucia Reining, and David M. Ceperley. Interacting Electrons: Theory and Computational Approaches. Cambridge University Press, 2016.

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30

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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31

Computational Approaches in Molecular Radiation Biology: Monte Carlo Methods (Basic Life Sciences). Springer, 1995.

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32

M, Neal Donald, and Langley Research Center, eds. A statistical simulation approach to safe life fatigue analysis of redundant metallic components. National Aeronautics and Space Administration, Langley Research Center, 1997.

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33

M, Neal Donald, and Langley Research Center, eds. A statistical simulation approach to safe life fatigue analysis of redundant metallic components. National Aeronautics and Space Administration, Langley Research Center, 1997.

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34

Kroese, Dirk P., and Reuven Y. Rubinstein. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Information Science and Statistics). Springer, 2004.

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35

An approach to simulation of extreme conditions for a planetary lander. National Aeronautics and Space Administration, Langley Research Center, 2001.

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36

Ohira, Toru. A master equation approach to stochastic neurodynamics. 1993.

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37

Boudreau, Joseph F., and Eric S. Swanson. Parallel computing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0009.

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This chapter describes various approaches to concurrency, or “parallel programming”. An overview of high performance computing is followed with a review of Flynn’s taxonomy of parallel computing. Three methods for implementing parallel code using the frameworks provided by MPI, openMP, and C++ threads are presented. The use of the C++ constructs mutex and future to resolve issues of synchronization are discussed. All methods are illustrated with an embarrassingly parallel application to a Monte Carlo integral and common pitfalls are presented. The chapter closes with a discussion and example o

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38

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-

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39

Morawetz, Klaus. Nonlocal Collision Integral. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797241.003.0013.

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The kinetic equation with the nonlocal shifts is presented as the final result on the way to derive the kinetic equation with nonlocal corrections. The exclusive dependence of the nonlocal and non-instant corrections on the scattering phase shift confirms the results from the theory of gases. With the approximation on the level of the Brueckner reaction matrix, the corresponding non-instant and nonlocal scattering integral in parallel with the classical Enskog’s equation, can be treated with Monte-Carlo simulation techniques. Neglecting the shifts, the Landau theory of quasiparticle transport

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40

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Graphs with hard constraints: further applications and extensions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0007.

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This chapter looks at further topics pertaining to the effective use of Markov Chain Monte Carlo to sample from hard- and soft-constrained exponential random graph models. The chapter considers the question of how moves can be sampled efficiently without introducing unintended bias. It is shown mathematically and numerically that apparently very similar methods of picking out moves can give rise to significant differences in the average topology of the networks generated by the MCMC process. The general discussion in complemented with pseudocode in the relevant section of the Algorithms chapte

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41

Lopes, Hedibert, and Nicholas Polson. Analysis of economic data with multiscale spatio-temporal models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.12.

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This article discusses the use of Bayesian multiscale spatio-temporal models for the analysis of economic data. It demonstrates the utility of a general modelling approach for multiscale analysis of spatio-temporal processes with areal data observations in an economic study of agricultural production in the Brazilian state of Espìrito Santo during the period 1990–2005. The article first describes multiscale factorizations for spatial processes before presenting an exploratory multiscale data analysis and explaining the motivation for multiscale spatio-temporal models. It then examines the temp

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42

Levin, Ines, and Betsy Sinclair. Causal Inference with Complex Survey Designs. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.

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This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of

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43

Melnikov, D. V., J. Kim, L. X. Zhang, and J. P. Leburton. Few-electron quantum-dot spintronics. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.013.2.

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This article examines the spin and charge properties of double and triple quantum dots (QDs) populated containing just a few electrons, with particular emphasis on laterally coupled QDs. It first describes the theoretical approach, known as exact diagonalization method, utilized on the example of the two-electron system in coupled QDs that are modelled as two parabolas. The many-body problem is solved via the exact diagonalization method as well as variational Heitler–London and Monte Carlo methods. The article proceeds by considering the general characteristics of the two-electron double-QD s

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44

Wright, A. G. Statistical processes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.003.0004.

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Two statistical processes affect performance: one concerns photon detection at the photocathode (binomial); and the other, gain at each dynode (Poisson). The combined statistical processes dictate resolution, both timing and pulse height. They are best examined using generating functions that are both elegant and capable of providing answers more efficiently than traditional approaches. The requirement for steady and pulsed light sources is an important one for testing and setting up procedures. The use of moments to test the quality of performance is illustrated for a steady DC light source.

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45

Donovan, Therese, and Ruth M. Mickey. Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.001.0001.

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Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journ

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46

Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic

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47

Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic

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