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Books on the topic 'Monte Carlo sampling and estimation'

Author: Grafiati
Published: 21 December 2024

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1

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

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2

Fu, Michael. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston, MA: Springer US, 1997.

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3

Fu, Michael. Conditional Monte Carlo: Gradient estimation and optimization applications. Boston: Kluwer Academic Publishers, 1997.

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4

Evans, Michael J. Monte Carlo computation of marginal posterior qualities. Toronto: University of Toronto, Dept. of Statistics, 1988.

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5

Manly, Bryan F. J., 1944-, ed. Randomization, bootstrap and Monte Carlo methods in biology. 2nd ed. London: Chapman & Hall, 1997.

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6

Manly, Bryan F. J. Randomization, bootstrap and Monte Carlo methods in biology. 2nd ed. Boca Raton, Fla: Chapman and Hall/CRC, 2001.

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7

Evans, Michael J. Adaptive importance sampling and chaining. Toronto: University of Toronto, Dept. of Statistics, 1990.

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8

Bosá, Ivana. Exact property estimation from diffusion Monte Carlo with minimal stochastic reconfiguration. St. Catharines, Ont: Brock University, Dept. of Physics, 2004.

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9

Aït-Sahalia, Yacine. Maximum likelihood estimation of stochastic volatility models. Cambridge, MA: National Bureau of Economic Research, 2004.

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10

Petrone, Sonia. A note on convergence rates of Gibbs sampling for nonparametric mixtures. Toronto: University of Toronto, Dept. of Statistics, 1998.

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11

Neal, Radford M. Markov chain Monte Carlo methods based on "slicing" the density function. Toronto: University of Toronto, Dept. of Statistics, 1997.

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12

Racine, J. S. Semiparamteric estimation in the presence of heteroskedasticity of unknown form. Toronto, Ont: Dept. of Economics, York University, 1989.

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13

Zhao, Zhong. Sensitivity of propensity score methods to the specifications. Bonn, Germany: IZA, 2005.

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14

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

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15

Srivastava, M. S. Classification with a preassigned error rate when two covariance matrices are equal. Toronto: University of Toronto, Dept. of Statistics, 1998.

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16

Martin, Christopher. Using equilibrium models on disequilibrium data: Some Monte-Carlo evidence on estimation and testing. London: University College, 1987.

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17

Christopher, Martin. Using equilibrium models on disequilibrium data: Some Monte-Carlo evidence on estimation and testing. London: Birkbeck College, [Dept. of Economics], 1987.

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18

Dietrich, Jason Lynn. How low can you go?: An optimal sampling strategy for fair lending exams. Washington, DC: Office of the Comptroller of the Currency, 2001.

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19

Heinz, Erzberger, and Ames Research Center, eds. Conflict probablility estimation for free flight. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1996.

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20

Böhning, Dankmar. On minimizing chi-square distances under the hypothesis of homogeneity of independence for a two-way contingency table. Osnabrück: Fachbereich Psychologie, Universität Osnabrück, 1985.

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21

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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22

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

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23

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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24

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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25

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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26

Haldrup, Niels. Seasonal integration and cointegration: A Monte Carlo study on the implications of seasonality for estimation and testing of long run relationships through static regressions. [s.l.]: typescript, 1988.

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27

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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28

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

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29

Monte Carlo and Quasi-Monte Carlo Sampling. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-78165-5.

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30

Monte Carlo and Quasi-Monte Carlo Sampling (Springer Series in Statistics). Springer, 2009.

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31

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Markov Chain Monte Carlo sampling of graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0006.

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This chapter looks at Markov Chain Monte Carlo techniques to generate hard- and soft-constrained exponential random graph ensembles. The essence is to define a Markov chain based on ergodic randomization moves acting on a network with transition probabilities which satisfy detailed balance. This is sufficient to ensure that the Markov chain will sample from the ensemble with the desired probabilities. This chapter studies several commonly seen randomization move sets and carefully defines acceptance probabilities for a range of different ensembles using both the Metropolis–Hastings and the Glauber prescription. Particular care is paid to describe and avoid the pitfalls that can occur in defining randomization moves for hard-constrained ensembles, and applying them without introducing inadvertent bias (i.e. defining and comparing protocols including switch-and-hold and mobility).
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32

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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33

Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Springer, 2011.

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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

Randomization and Monte Carlo methods in biology. London: Chapman and Hall, 1991.

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36

Manly, Bryan F. J. Randomization, Bootstrap and Monte Carlo Methods in Biology. Taylor & Francis Group, 2018.

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37

Manly, Bryan F. J. Randomization, Bootstrap and Monte Carlo Methods in Biology. Taylor & Francis Group, 2018.

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38

Manly, Bryan F. J., and Jorge A. Navarro Alberto. Randomization, Bootstrap and Monte Carlo Methods in Biology. Taylor & Francis Group, 2020.

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39

Manly, Bryan F. J., and Jorge A. Navarro Alberto. Randomization, Bootstrap and Monte Carlo Methods in Biology. Taylor & Francis Group, 2020.

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40

Manly, Bryan F. J., and Jorge A. Navarro Alberto. Randomization, Bootstrap and Monte Carlo Methods in Biology. Taylor & Francis Group, 2020.

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41

Manly, Bryan F. J., and Jorge A. Navarro Alberto. Randomization, Bootstrap and Monte Carlo Methods in Biology. Taylor & Francis Group, 2020.

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42

Manly, Bryan F. J., and Jorge A. Navarro Alberto. Randomization, Bootstrap and Monte Carlo Methods in Biology. Taylor & Francis Group, 2020.

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43

Manly, Bryan F. J., and Jorge A. Navarro Alberto. Randomization, Bootstrap, and Monte Carlo Methods in Biology. CRC Press LLC, 2022.

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44

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2008.

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45

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2016.

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46

Randomization, bootstrap and Monte Carlo methods in biology. 3rd ed. Boca Raton, FL: Chapman & Hall/ CRC, 2007.

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47

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2016.

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48

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2011.

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49

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2016.

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50

Rubinstein, Reuven Y. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2009.

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