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Книги з теми "Sampling with loaded probabilities"

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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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1

Gurao, Dr Rajendra G. Sampling Methods. Kanpur, India: Chandralok Prakashan, 2015.

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2

G, Meeden, ed. Bayesian methods for finite population sampling. London: Chapman & Hall, 1997.

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3

1931-, MacNeill Ian B., and Umphrey Gary J. 1953-, eds. Applied probability, stochastic processes, and sampling theory. Dordrecht: D. Reidel, 1987.

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4

Wright, Tommy. Exact confidence bounds when sampling from small finite universes: An easy reference based on the hypergeometric distribution. Berlin: Springer-Verlag, 1991.

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5

Introduction to empirical processes and semiparametric inference. New York: Springer, 2008.

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6

Gupta, A. K. Theory of sample surveys. New Jersey: World Scientific, 2011.

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7

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

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8

Miliken, George A. Analysis of messy data. New York: Chapman & Hall, 1992.

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9

R.C. Bose Symposium (1988 New Delhi). Probability, statistics and design of experiments. New Delhi: Wiley Eastern, 1990.

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10

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

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11

A, Levin Bruce, and Paik Myunghee Cho, eds. Statistical methods for rates and proportions. 3rd ed. Hoboken, N.J: J. Wiley, 2003.

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12

Gail, Burrill, and National Council of Teachers of Mathematics., eds. Navigating through data analysis in grades 9-12. Reston, VA: National Council of Teachers of Mathematics, 2003.

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13

Zanella, Angelo, Benito V. Frosini, Umberto Magagnoli, and Giuseppe Boari. Studi in onore di Angelo Zanella. Milano: V&P università, 2002.

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14

P, Kroese Dirk, ed. Simulation and the monte carlo method. 2nd ed. Hoboken, N.J: John Wiley & Sons, 2008.

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15

Brewer, K. R. W., and M. Hanif. Sampling With Unequal Probabilities. Springer, 2013.

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16

Brewer, K. R. W., and M. Hanif. Sampling with Unequal Probabilities. Springer London, Limited, 2013.

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17

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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18

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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19

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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20

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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21

Advances in the Statistical Sciences: Applied Probability, Stochastic Processes, and Sampling Theory: Volume I of the Festschrift in Honor of Professor ... Ontario Series in Philosophy of Science). Springer, 1986.

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22

Wright, Tommy. Exact Confidence Bounds When Sampling from Small Finite Universes: An Easy Reference Based on the Hypergeometric Distribution. Springer London, Limited, 2012.

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23

Introduction to Probability Simulation and Gibbs Sampling with R. Springer London, Limited, 2010.

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24

Kosorok, Michael R. Introduction to Empirical Processes and Semiparametric Inference. Springer New York, 2010.

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25

Sampling Techniques: Methods and Applications. New York, USA: Nova Science Publishers Inc, 2018.

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26

Wright, Tommy. Exact Confidence Bounds when Sampling from Small Finite Universes: An Easy Reference Based on the Hypergeometric Distribution. Springer, 2011.

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27

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

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28

Fleiss, Joseph L., Bruce Levin, and Myunghee Cho Paik. Statistical Methods for Rates and Proportions. Wiley & Sons Australia, Limited, John, 2001.

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29

Fleiss, Joseph L. Statistical Methods for Rates & Proportions. 2nd ed. Wiley-Interscience, 2000.

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30

The Theory Of Sample Surveys And Statistical Decisions. Pitam Pura, New Delhi, India: New India Publishing Agency, 2009.

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31

Suess, Eric A., and Bruce E. Trumbo. Gibbs Sampling and Screening Tests: From Random Numbers to the Gibbs Sampler (Springer Texts in Statistics). Springer, 2006.

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32

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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Анотація:
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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33

Project, School Mathematics. Living with Uncertainty. Cambridge University Press, 1993.

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34

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

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35

Probability And Statistics For Economists. New Jersey, USA: WSPC, 2017.

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36

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.

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Анотація:
This chapter presents some theoretical tools for defining random graph ensembles systematically via soft or hard topological constraints including working through some properties of the Erdös-Rényi random graph ensemble, which is the simplest non-trivial random graph ensemble where links appear between two nodes with a fixed probability p. The chapter sets out the central representation of graph generation as the result of a discrete-time Markovian stochastic process. This unites the two flavours of graph generation approaches – because they can be viewed as simply moving forwards or backwards through this representation. It is possible to define a random graph by an algorithm, and then calculate the associated stationary probability. The alternative approach is to specify sampling weights and then to construct an algorithm that will have these weights as the stationary probabilities upon convergence.
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37

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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Анотація:
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 chapter, which explicitly sets out some accurate and practical move sampling approaches. The chapter also describes how the MCMC equilibrium probabilities can be purposely deformed to, for example, target desired correlations between degrees of connected nodes. The mathematical exposition is complemented with graphs showing the results of numerical simulations.
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38

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

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Анотація:
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 potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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39

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.

Повний текст джерела
Анотація:
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 potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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