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1.

図書
図書
1. Recent progress in quantum Monte Carlo
Shigenori Tanaka, Pierre-Nicholas Roy, Lubos Mitas, editor[s] ; sponsored by the ACS Division of Physical Chemistry
出版情報: Washington, DC : American Chemical Society , [Oxford] : Distributed in print by Oxford University Press, c2016  ix, 173 p. ; 24 cm
シリーズ名: ACS symposium series ; 1234
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2.

図書
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2. Monte Carlo methods in financial engineering
Paul Glasserman
出版情報: New York ; Tokyo : Springer, c2004  xiii, 596 p. ; 25 cm
シリーズ名: Applications of mathematics ; 53
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目次情報: 続きを見る
Foundations
Generating Random Numbers and Random Variables
Generating Sample Paths
Variance Reduction Techniques
Quasi-Monte Carlo Methods
Discretization Methods
Estimating Sensitivities
Pricing American Options
Applications in Risk Management
Appendices
Foundations
Generating Random Numbers and Random Variables
Generating Sample Paths
3.

図書
図書
3. Monte Carlo methods in statistical physics (Berlin.)
with contributions by K. Binder ... [et al.] ; edited by K. Binder
出版情報: Berlin ; New York : Springer-Verlag, 1979  xiii, 376 p. ; 25 cm
シリーズ名: Topics in current physics ; v. 7
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4.

図書
図書
4. Monte Carlo methods in statistical physics. 2nd ed (: us ; : gw)
edited by K. Binder ; with contributions by K.Binder ... [et al.]
出版情報: Berlin ; Heidelberg ; New York ; Tokyo : Springer-Verlag, c1986  xv, 411 p. ; 25 cm
シリーズ名: Topics in current physics ; v. 7
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5.

図書
図書
5. Simulation and the Monte Carlo method
Reuven Y. Rubinstein
出版情報: New York : Wiley, c1981  xv, 278 p. ; 24 cm
シリーズ名: Wiley series in probability and mathematical statistics ; . Probability and mathematical statistics
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Systems, Models, Simulation and the Monte Carlo Method
Random Number Generation
Random Variate Generation
Monte Carlo Integration and Variance Reduction Techniques
Linear Equations and Markov Chains
Regenerative Method for Simulation Analysis
Monte Carlo Optimization
Appendix
Exercises
References
Index
Systems, Models, Simulation and the Monte Carlo Method
Random Number Generation
Random Variate Generation
6.

図書
図書
6. Approximating integrals via Monte Carlo and deterministic methods
Michael Evans and Tim Swartz
出版情報: Oxford ; New York : Oxford University Press, c2000  ix, 288 p. ; 24 cm
シリーズ名: Oxford statistical science series ; 20
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7.

図書
図書
7. A guide to Monte Carlo simulations in statistical physics (: hard ; : pbk)
David P. Landau, Kurt Binder
出版情報: Cambridge : Cambridge University Press, 2000  xiii, 384 p. ; 25 cm
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Preface
Introduction / 1：
Some necessary background / 2：
Simple sampling Monte Carlo methods / 3：
The metropolis Monte Carlo algorithm for lattice models / 4：
More on importance sampling Monte Carlo methods for lattice systems / 5：
Off-lattice models / 6：
Reweighting methods / 7：
Quantum Monte Carlo methods / 8：
Monte Carlo renormalization group methods / 9：
Non-equilibrium and irreversible processes / 10：
Lattice gauge models: a brief introduction / 11：
A brief review of other methods of computer simulation / 12：
Outlook / 13：
Preface
Introduction / 1：
Some necessary background / 2：
8.

図書
図書
8. Monte Carlo and quasi-Monte Carlo Methods 1996 : proceedings of a conference at the University of Salzburg, Austria, July 9-12, 1996
Harald Niederreiter ... [et al.] (editors)
出版情報: New York ; Tokyo : Springer, 1998  xii, 448 p. ; 24 cm
シリーズ名: Lecture notes in statistics ; 127
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9.

図書
図書
9. Disordered alloys : diffuse scattering and Monte Carlo simulations
Werner Schweika
出版情報: Berlin : Springer, c1998  x, 122 p. ; 24 cm
シリーズ名: Springer tracts in modern physics : Ergebnisse der exakten Naturwissenschaften / editor, G. Höhler ; 141
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10.

図書
図書
10. Quantum Monte Carlo methods in physics and chemistry (: pbk)
edited by M.P. Nightingale and C.J. Umrigar
出版情報: Dordrecht : Kluwer Academic, c1999  viii, 467 p. ; 25 cm
シリーズ名: NATO science series ; Ser. C . Mathematical and physical sciences ; v. 525
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11.

図書
図書
11. Markov chain Monte Carlo in practice. 1st ed
edited by W.R. Gilks, S. Richardson, and D.J. Spiegelhalter
出版情報: London ; Tokyo : Chapman & Hall, 1996  xvii, 486 p. ; 25 cm
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12.

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12. Basics
Malvin H. Kalos, Paula A. Whitlock
出版情報: New York : J. Wiley, c1986  ix, 186 p. ; 24 cm
シリーズ名: A Wiley-Interscience publication ; . Monte Carlo methods ; v. 1
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What is Monte Carlo?
A Bit of Probability Theory
Sampling Random Variables
Monte Carlo Evaluation of Finite-Dimensional Integrals
Statistical Physics
Simulations of Stochastic Systems: Radiation Transport
Random Walks and Integral Equations
Introduction to Green's Function Monte Carlo
Appendixes
Index
What is Monte Carlo?
A Bit of Probability Theory
Sampling Random Variables
13.

図書
図書
13. The Monte Carlo method in condensed matter physics (: us ; : gw)
edited by K. Binder ; with contributions by A. Baumgärtner ... [et al.]
出版情報: Berlin ; Tokyo : Springer-Verlag, c1992  xvi, 392 p. ; 25 cm
シリーズ名: Topics in applied physics ; v. 71
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14.

図書
図書
14. Optimization of weighted Monte Carlo methods (: us ; : gw)
Gennadii A. Mikhailov ; translated by Karl K. Sabelfeld
出版情報: Berlin ; New York ; Tokyo : Springer-Verlag, c1992  xi, 225 p. ; 24 cm
シリーズ名: Springer series in computational physics
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15.

図書
図書
15. Monte Carlo simulations of disordered systems
S. Jain
出版情報: Singapore ; Teaneck, N.J. : World Scientific, 1992  xii, 179 p. ; 23 cm
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16.

図書
図書
16. Monte Carlo principles and neutron transport problems
[by] Jerome Spanier and Ely M. Gelbard
出版情報: Reading, Mass. : Addison-Wesley Pub. Co, 1969  xiv, 234 p ; 24 cm
シリーズ名: Addison-Wesley series in computer science and information processing
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17.

図書
図書
17. Finite size scaling and numerical simulation of statistical systems
editor, V. Privman
出版情報: Singapore ; Teaneck : World Scientific, c1990  ix, 518 p. ; 23 cm
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18.

図書
図書
18. Computational probability and simulation (pbk)
Sidney J. Yakowitz
出版情報: Reading, Mass. : Addison-Wesley Pub. Co., Advanced Book Program, 1977  xxii, 240 p. ; 25 cm
シリーズ名: Applied mathematics and computation : a series of graduate textbooks, monographs, reference works / Series Editor Robert Kalaba ; no.12
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19.

図書
図書
19. The Monte Carlo method : the method of statistical trials. 1st English ed
N.P. Buslenko ... [et al.] ; edited by Yu.A. Shreider ; translated from the Russian by G.J. Tee ; translation edited by D.M. Parkyn
出版情報: Oxford : Pergamon, 1966  xii, 381 p. ; 23 cm
シリーズ名: International series of monographs on pure and applied mathematics ; v. 87
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20.

図書
図書
20. The Monte Carlo methods in atmospheric optics (: us ; : gw)
by G.I. Marchuk ... [et al.]
出版情報: Berlin ; New York : Springer-Verlag, 1980  viii, 208 p. ; 24 cm
シリーズ名: Springer series in optical sciences ; 12
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21.

図書
図書
21. Computer simulation of liquids
M.P. Allen and D.J. Tildesley
出版情報: Oxford [Oxfordshire] : Clarendon Press , New York : Oxford University Press, 1987  xix, 385 p. ; 24 cm
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Introduction
Statistical mechanics
Molecular dynamics
Monte Carlo methods
Some tricks of the trade
How to analyse the results
Advanced simulation techniques
Non-equilibrium molecular dynamics
Brownian dynamics
Quantum simulations
Some applications
Appendices
References
Index
Introduction
Statistical mechanics
Molecular dynamics
22.

図書
図書
22. Simulation of liquids and solids : molecular dynamics and Monte Carlo methods in statistical mechanics (pbk.)
editors, Giovanni Ciccotti, Daan Frenkel, Ian R. McDonald
出版情報: Amsterdam ; Tokyo : North-Holland , New York, N.Y., U.S.A. : Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co., 1987  xii, 481 p. ; 23 cm
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23.

図書
図書
23. Numerical methods for stochastic processes
Nicolas Bouleau, Dominique Lépingle
出版情報: New York : Wiley, c1994  xvii, 359 p. ; 25 cm
シリーズ名: Wiley series in probability and mathematical statistics ; . Applied probability and statistics
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Preliminaries
Computation of Expectations in Finite Dimension
Simulation of Random Processes
Deterministic Resolution of Some Markovian Problems
Stochastic Differential Equations and Brownian Functionals
Notes
References
Index
Preliminaries
Computation of Expectations in Finite Dimension
Simulation of Random Processes
24.

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図書
24. Image analysis, random fields and dynamic Monte Carlo methods : a mathematical introduction (: gw ; : us)
Gerhard Winkler
出版情報: Berlin ; New York : Springer-Verlag, c1995  xiv, 324 p. ; 24 cm
シリーズ名: Applications of mathematics ; 27
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25.

図書
図書
25. Image analysis, random fields and Markov chain Monte Carlo methods : a mathematical introduction. 2nd ed
Gerhard Winkler
出版情報: Berlin ; Tokyo : Springer, c2003  xvi, 387 p. ; 25 cm.
シリーズ名: Applications of mathematics ; 27
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目次情報: 続きを見る
Introduction
Bayesian Image Analysis: Introduction / Part I：
The Bayesian Paradigm / 1：
Warming up for Absolute Beginners / 1.1：
linages and Observations / 1.2：
Prior and Posterior Distributions / 1.3：
Bayes Estimators / 1.4：
Cleaning Dirty Pictures / 2：
Boundaries and Their Information Content / 2.1：
Towards Piccewiso Smoothing / 2.2：
Filters, Smoothers, and Bayes Estimators / 2.3：
Boundary Extraction / 2.4：
Dependence on Hyperparameters / 2.5：
Finite Random Fields / 3：
Markov Random Fields / 3.1：
Gibbs Fields and Potentials / 3.2：
Potentials Continued / 3.3：
The Gibbs Sampler and Simulated Annealing / Part II：
Markov Chains: Limit Theorems / 4：
Preliminaries / 4.1：
The Contraction Coefficient / 4.2：
Homogeneous Markov Chains / 4.3：
Exact Sampling / 4.4：
Inhomogeneous Markov Chains / 4.5：
A Law of Large Numbers for Inhomogeneous Chains / 4.6：
A Counterexample for the Law of Large Numbers / 4.7：
Gibbsian Sampling and Annealing / 5：
Sampling / 5.1：
Simulated Annealing / 5.2：
Discussion / 5.3：
Cooling Schedules / 6：
The ICM Algorithm / 6.1：
Exact MAP Estimation Versus Fast Cooling / 6.2：
Finite Time Annealing / 6.3：
Variations of the Gibbs Sampler / Part III：
Gibbsian Sampling and Annealing Revisited / 7：
A General Gibbs Sampler / 7.1：
Sampling and Annealing Under Constraints / 7.2：
Partially Parallel Algorithms / 8：
Synchronous Updating on Independent Sets / 8.1：
The Swendson-Wang Algorithm / 8.2：
Synchronous Algorithms / 9：
Invariant Distributions and Convergence / 9.1：
Support of the Limit Distribution / 9.2：
Synchronous Algorithms and Reversibility / 9.3：
Metropolis Algorithms and Spectral Methods / Part IV：
Metropolis Algorithms / 10：
Metropolis Sampling and Annealing / 10.1：
Convergence Theorems / 10.2：
Best Constants / 10.3：
About Visiting Schemes / 10.4：
Generalizations and Modifications / 10.5：
The Metropolis Algorithm in Combinatorial Optimization / 10.6：
The Spectral Gap and Convergence of Markov Chains / 11：
Eigenvalues of Markov Kernels / 11.1：
Geometric Convergence Rates / 11.2：
Eigenvalues, Sampling, Variance Reduction / 12：
Samplers and Their Eigenvalues / 12.1：
Variance Reduction / 12.2：
Importance Sampling / 12.3：
Continuous Time Processes / 13：
Discrete State Space / 13.1：
Continuous State Space / 13.2：
Texture Analysis / Part V：
Partitioning / 14：
How to Tell Textures Apart / 14.1：
Bayesian Texture Segmentation / 14.2：
Segmentation by a Boundary Model / 14.3：
Julesz's Conjecture and Two Point Processes / 14.4：
Random Fields and Texture Models / 15：
Neighbourhood Relations / 15.1：
Random Field Texture Models / 15.2：
Texture Synthesis / 15.3：
Bayesian Texture Classification / 16：
Contextual Classification / 16.1：
Marginal Posterior Modes Methods / 16.2：
Parameter Estimation / Part VI：
Maximum Likelihood Estimation / 17：
The Likelihood Function / 17.1：
Objective Functions / 17.2：
Consistency of Spatial ML Estimators / 18：
Observation Windows and Specifications / 18.1：
Pseudolikelihood Methods / 18.2：
Large Deviations and Full Maximum Likelihood / 18.3：
Partially Observed Data / 18.4：
Computation of Pull ML Estimators / 19：
A Naive Algorithm / 19.1：
Stochastic Optimization for the Full Likelihood / 19.2：
Main Results / 19.3：
Error Decomposition / 19.4：
Supplement / 19.5：
A Glance at Neural Networks / 20：
Boltzmann Machines / 20.1：
A Learning Rule / 20.2：
Three Applications / 21：
Motion Analysis / 21.1：
Tomographic Image Reconstruction / 21.2：
Biological Shape / 21.3：
Appendix / Part VIII：
Simulation of Random Variables / A：
Pseudorandom Numbers / A.1：
Discrete Random Variables / A.2：
Special Distributions / A.3：
Analytical Tools / B：
Concave Functions / B.1：
Convergence of Descent Algorithms / B.2：
A Discrete Gronwall Lemma / B.3：
A Gradient System / B.4：
Physical Imaging Systems / C：
The Software Package AntsInFields / D：
References
Symbols
Index
Introduction
Bayesian Image Analysis: Introduction / Part I：
The Bayesian Paradigm / 1：
26.

図書
図書
26. Monte Carlo methods. Repr. with minor corrections
J.M. Hammersley and D.C. Handscomb
出版情報: London : Methuen, 1965, c1964  vii, 178 p. ; 20 cm
シリーズ名: Methuen's monographs on applied probability and statistics
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27.

図書
図書
27. Sequential Monte Carlo methods in practice
Arnaud Doucet, Nando de Freitas, Neil Gordon, editors ; foreword by Adrian Smith
出版情報: New York : Springer, c2001  xxvii, 581 p. ; 24 cm
シリーズ名: Statistics for engineering and information science
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28.

図書
図書
28. Monte Carlo statistical methods. 2nd ed
Christian P. Robert, George Casella
出版情報: New York : Springer, c2004  xxx, 645 p. ; 24 cm
シリーズ名: Springer texts in statistics
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29.

図書
図書
29. Monte Carlo frameworks : building customisable high-performance C++ applications (: hbk)
Daniel J. Duffy, Jörg Kienitz
出版情報: Chichester, U.K. : Wiley, 2009  xxv, 750 p. ; 26 cm.
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目次情報: 続きを見る
Preface
My First Monte Carlo Application One-Factor Problems / Chapter 0：
Mathematical Preparations for the Monte Carlo Method / Chapter 1：
The Mathematics of Stochastic Differential Equations (SDE) / Chapter 2：
Alternative SDEs and Toolkit Functionality / Chapter 3：
An Introduction to the Finite Difference Method for SDE / Chapter 4：
Design and Implementation of Finite Difference Schemes in Computational Finance / Chapter 5：
Advanced Finance Models and Numerical Methods / Chapter 6：
Architectures and Frameworks for Monte Carlo Methods: Overview / Chapter 8：
System Decomposition and System Patterns / Chapter 9：
Detailed Design using the GOF Patterns / Chapter 10：
Combining Object-Oriented and Generic Programming Models / Chapter 11：
Data Structures and their Application to the Monte Carlo Method / Chapter 12：
The Boost Library: An Introduction / Chapter 13：
C++ Application Optimisation and Performance Improvement / Chapter 21：
An Introduction to Multi-threaded and Parallel Programming / Chapter 24：
An Introduction to OpenMP and its Applications to the Monte Carlo Method / Chapter 25：
Excel, C++ and Monte Carlo Integration / Chapter 27：
Preface
My First Monte Carlo Application One-Factor Problems / Chapter 0：
Mathematical Preparations for the Monte Carlo Method / Chapter 1：
30.

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図書
30. Monte Carlo simulation in statistical physics : an introduction. 5th ed
Kurt Binder, Dieter W. Heermann
出版情報: Berlin : Springer Verlag, c2010  xiv, 200 p. ; 24 cm
シリーズ名: Graduate texts in physics
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31.

図書
図書
31. Applications of the Monte Carlo method in statistical physics (: us ; : gw)
edited by K. Binder ; with contributions by A. Baumgärtner ... [et al.]
出版情報: Berlin ; New York : Springer-Verlag, 1984  xiv, 311 p. ; 25 cm
シリーズ名: Topics in current physics ; 36
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32.

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32. Monte Carlo methods in Bayesian computation
Ming-Hui Chen, Qi-Man Shao, Joseph G. Ibrahim
出版情報: New York : Springer, c2000  xiii, 386 p. ; 25 cm
シリーズ名: Springer series in statistics
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33.

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図書
33. Monte Carlo methods in fuzzy optimization
James J. Buckley and Leonard J. Jowers
出版情報: Berlin : Springer, c2008  xiii, 260 p. ; 25 cm
シリーズ名: Studies in fuzziness and soft computing ; 222
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34.

図書
図書
34. Monte Carlo methods in statistical physics (: hbk ; : pbk)
M.E.J. Newman and G.T. Barkema
出版情報: Oxford [Oxfordshire] : Clarendon Press, 1999  xiv, 475 p. ; 24 cm
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Equilibrium Monte Carlo calculations / I：
Introduction / 1：
The principles of equilibrium thermal Monte Carlo simulations / 2：
The Ising model and the Metropolis algorithm / 3：
Other algorithms for the Ising model / 4：
The conserved-order-parameter Ising model / 5：
Disordered spin models / 6：
Ice models / 7：
Analysing Monte Carlo data / 8：
Out-of-equilibrium calculations / II：
Principles of out-of-equilibrium Monte Carlo simulation / 9：
Non-equilibrium simulations of the Ising model / 10：
Monte Carlo simulations in surface science / 11：
The repton model / 12：
Implementation / III：
Lattices and data structures / 13：
Monte Carlo simulations on parallel computers / 14：
Multispin coding / 15：
Random numbers / 16：
Equilibrium Monte Carlo calculations / I：
Introduction / 1：
The principles of equilibrium thermal Monte Carlo simulations / 2：
35.

図書
図書
35. Interdependent systems : structure and estimation (: American Elsevier ; : ne)
by Ernest J. Mosbaek and Herman O. Wold ; with contributions by Ejnar Lyttkens, Anders Ågren and Lennart Bodin
出版情報: Amsterdam : North-Holland Pub. Co., 1970, c1969  xxii, 542 p ; 25 cm
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36.

図書
図書
36. Statistical techniques in simulation (pt. 1 ; pt. 2)
Jack P.C. Kleijnen
出版情報: New York : M. Dekker, 1974-c1975  2 v. (xv, 775 p.) ; 24 cm
シリーズ名: Statistics : textbooks and monographs ; v. 9
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37.

図書
図書
37. Applications of the Monte Carlo method in statistical physics. 2nd ed (: U.S. ; : Germany)
edited by K. Binder, with contributions by A. Baumgärtner ... [et al.]
出版情報: Berlin ; Tokyo : Springer-Verlag, c1987  xvi, 341 p. ; 25 cm
シリーズ名: Topics in current physics ; 36
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38.

電子ブック
ＥＢ
38. Quantum Monte Carlo approaches for correlated systems (: electronic bk)
Federico Becca , Sandro Sorella
出版情報: Cambridge Core  1 online resource (xii, 274 p.)
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39.

電子ブック
ＥＢ
39. Nonequilibrium gas dynamics and molecular simulation (: electronic bk)
Iain D. Boyd, University of Michigan, Thomas E. Schwartzentruber, University of Minnesota
出版情報:   1 online resource (xxi, 360 p.)
シリーズ名: Cambridge aerospace series ; 42
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40.

電子ブック
ＥＢ
40. Interacting electrons : theory and computational approaches (: electronic bk)
Richard M. Martin, Lucia Reining, David M. Ceperley
出版情報:   1 online resource (xxiv, 818 p.)
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41.

電子ブック
ＥＢ
41. Quantum Monte Carlo methods : : algorithms for lattice models (: electronic bk)
J.E. Gubernatis, N. Kawashima, P. Werner
出版情報:   1 online resource (xiii, 488 p.)
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42.

電子ブック
ＥＢ
42. Advanced Markov chain Monte Carlo methods : learning from past samples (: electronic bk)
Faming Liang, Chuanhai Liu, Raymond J. Carroll
出版情報: [S.l.] : Wiley Online Library, [20--]  1 online resource (xix, 357 p.)
シリーズ名: Wiley series in computational statistics
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目次情報: 続きを見る
Preface
Acknowledgements
List of Figures
List of Tables
Bayesian Inference and Markov chain Monte Carlo / 1：
Bayes / 1.1：
Bayes output / 1.2：
Monte Carlo Integration / 1.3：
Random variable generation / 1.4：
Markov chain Monte Carlo / 1.5：
Exercises
The Gibbs sampler / 2：
Data Augmentation / 2.1：
Implementation strategies and acceleration methods / 2.3：
Applications / 2.4：
The Metropolis-Hastings Algorithm / 3：
Some Variants of the Metropolis-Hastings Algorithm / 3.1：
Reversible Jump MCMC Algorithm for Bayesian Model Selection / 3.3：
Problems
Metropolis-within-Gibbs Sampler for ChIP-chip Data Analysis / 3.4：
Auxiliary Variable MCMC Methods / 4：
Simulated Annealing / 4.1：
Simulated Tempering / 4.2：
Slice Sampler / 4.3：
The Swendsen-Wang Algorithm / 4.4：
The Wolff Algorithm / 4.5：
The Mller algorithm / 4.6：
The Exchange Algorithm / 4.7：
Double MH Sampler / 4.8：
Monte Carlo MH Sampler / 4.9：
Population-Based MCMC Methods / 4.10：
Adaptive Direction Sampling / 5.1：
Conjugate Gradient Monte Carlo / 5.2：
Sample Metropolis-Hastings Algorithm / 5.3：
Parallel Tempering / 5.4：
Evolutionary Monte Carlo / 5.5：
Sequential Parallel Tempering for Simulation of High Dimensional / 5.6：
Systems
Equi-Energy Sampler / 5.7：
Forecasting / 5.8：
Dynamic Weighting / 6：
Dynamically Weighted Importance Sampling / 6.1：
Monte Carlo Dynamically Weighted Importance Sampling / 6.3：
Sequentially Dynamically Weighted Importance Sampling / 6.4：
Stochastic Approximation Monte Carlo / 7：
Multicanonical Monte Carlo / 7.1：
1/k-Ensemble Sampling / 7.2：
Wang-Landau Algorithm / 7.3：
Applications of Stochastic Approximation Monte Carlo / 7.4：
Variants of Stochastic Approximation Monte Carlo / 7.6：
Theory of Stochastic Approximation Monte Carlo / 7.7：
Trajectory Averaging: Toward the Optimal Convergence Rate / 7.8：
Markov Chain Monte Carlo with Adaptive Proposals / 8：
Stochastic Approximation-based Adaptive Algorithms / 8.1：
Adaptive Independent Metropolis-Hastings Algorithms / 8.2：
Regeneration-based Adaptive Algorithms / 8.3：
Population-based Adaptive Algorithms / 8.4：
References
Index
Acknowledgments
Publisher's Acknowledgments
Bayesian Inference and Markov Chain Monte Carlo
Specification of Bayesian Models / 1.1.1：
The Jeffreys Priors and Beyond / 1.1.2：
Bayes Output
Credible Intervals and Regions / 1.2.1：
Hypothesis Testing: Bayes Factors / 1.2.2：
The Problem / 1.3.1：
Monte Carlo Approximation / 1.3.2：
Monte Carlo via Importance Sampling / 1.3.3：
Random Variable Generation
Direct or Transformation Methods / 1.4.1：
Acceptance-Rejection Methods / 1.4.2：
The Ratio-of-Uniforms Method and Beyond / 1.4.3：
Adaptive Rejection Sampling / 1.4.4：
Perfect Sampling / 1.4.5：
Markov Chain Monte Carlo
Markov Chains / 1.5.1：
Convergence Results / 1.5.2：
Convergence Diagnostics / 1.5.3：
The Gibbs Sampler
Implementation Strategies and Acceleration Methods
Blocking and Collapsing / 2.3.1：
Hierarchical Centering and Reparameterization / 2.3.2：
Parameter Expansion for Data Augmentation / 2.3.3：
Alternating Subspace-Spanning Resampling / 2.3.4：
The Student-t Model / 2.4.1：
Robit Regression or Binary Regression with the Student-t Link / 2.4.2：
Linear Regression with Interval-Censored Responses / 2.4.3：
The EM and PX-EM Algorithms / Appendix 2A：
Independence Sampler / 3.1.1：
Random Walk Chains / 3.1.2：
Problems with Metropolis-Hastings Simulations / 3.1.3：
Variants of the Metropolis-Hastings Algorithm
The Hit-and-Run Algorithm / 3.2.1：
The Langevin Algorithm / 3.2.2：
The Multiple-Try MH Algorithm / 3.2.3：
Reversible Jump MCMC Algorithm for Bayesian Model Selection Problems
Reversible Jump MCMC Algorithm / 3.3.1：
Change-Point Identification / 3.3.2：
Metropolis-Within-Gibbs Sampler for ChIP-chip Data Analysis
Metropolis-Within-Gibbs Sampler / 3.4.1：
Bayesian Analysis for ChIP-chip Data / 3.4.2：
The Slice Sampler
The Møller Algorithm
The Double MH Sampler
Spatial Autologistic Models / 4.8.1：
Monte Carlo MH Algorithm / 4.9.1：
Convergence / 4.9.2：
Spatial Autologistic Models (Revisited) / 4.9.3：
Marginal Inference / 4.9.4：
Autonormal Models / 4.10.1：
Social Networks / 4.10.2：
Evolutionary Monte Carlo in Binary-Coded Space / 5.5.1：
Evolutionary Monte Carlo in Continuous Space / 5.5.2：
Implementation Issues / 5.5.3：
Two Illustrative Examples / 5.5.4：
Discussion / 5.5.5：
Sequential Parallel Tempering for Simulation of High Dimensional Systems
Build-up Ladder Construction / 5.6.1：
Sequential Parallel Tempering / 5.6.2：
An Illustrative Example: the Witch's Hat Distribution / 5.6.3：
Bayesian Curve Fitting / 5.6.4：
Protein Folding Simulations: 2D HP Model / 5.8.2：
Bayesian Neural Networks for Nonlinear Time Series Forecasting / 5.8.3：
Protein Sequences for 2D HP Models / Appendix 5A：
The IWIW Principle / 6.1.1：
Tempering Dynamic Weighting Algorithm / 6.1.2：
Dynamic Weighting in Optimization / 6.1.3：
The Basic Idea / 6.2.1：
A Theory of DWIS / 6.2.2：
Two DWIS Schemes / 6.2.3：
Weight Behavior Analysis / 6.2.5：
A Numerical Example / 6.2.6：
Sampling from Distributions with Intractable Normalizing Constants / 6.3.1：
Bayesian Analysis for Spatial Autologistic Models / 6.3.2：
The Wang-Landau Algorithm
Efficient p-Value Evaluation for Resampling-Based Tests / 7.5.1：
Bayesian Phylogeny Inference / 7.5.2：
Bayesian Network Learning / 7.5.3：
Smoothing SAMC for Model Selection Problems / 7.6.1：
Continuous SAMC for Marginal Density Estimation / 7.6.2：
Annealing SAMC for Global Optimization / 7.6.3：
Convergence Rate / 7.7.1：
Ergodicity and its IWIW Property / 7.7.3：
Trajectory Averaging for a SAMCMC Algorithm / 7.8.1：
Trajectory Averaging for SAMC / 7.8.2：
Proof of Theorems 7.8.2 and 7.8.3 / 7.8.3：
Test Functions for Global Optimization / Appendix 7At：
Stochastic Approximation-Based Adaptive Algorithms
Ergodicity and Weak Law of Large Numbers / 8.1.1：
Adaptive Metropolis Algorithms / 8.1.2：
Regeneration-Based Adaptive Algorithms
Identification of Regeneration Times / 8.3.1：
Proposal Adaptation at Regeneration Times / 8.3.2：
Population-Based Adaptive Algorithms
ADS, EMC, NKC and More / 8.4.1：
Adaptive.EMC / 8.4.2：
Application to Sensor Placement Problems / 8.4.3：
Preface
Acknowledgements
List of Figures
43.

図書
図書
43. The cross-entropy method : a unified approach to combinatorial optimization, Monte-Carlo simluation, and machine learning (: hbk)
Reuven Y. Rubinstein, Dirk P. Kroese
出版情報: New York : Springer, c2004  xx, 300 p. ; 24 cm
シリーズ名: Information science and statistics / series editors M. Jordan ... [et al.]
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44.

図書
図書
44. Markov chains and Monte Carlo calculations in polymer science
edited by George G. Lowry
出版情報: New York : M. Dekker, 1970  xi, 329 p. ; 24 cm
シリーズ名: Monographs in macromolecular chemistry
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45.

図書
図書
45. Monte Carlo methods in boundary value problems (: Berlin ; : New York)
Karl K. Sabelfeld
出版情報: Berlin ; Tokyo : Springer-Verlag, c1991  xii, 283 p. ; 25 cm
シリーズ名: Springer series in computational physics
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46.

電子ブック
ＥＢ
46. Supercritical fluid science and technology
Keith P. Johnson, editor, Johannes M.L. Penninger, editor
出版情報: [S.l.] : ACS Publications  1 online resource (x, 550 p.)
シリーズ名: ACS symposium series ; 406
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目次情報: 続きを見る
New Directions in Supercritical Fluid Science and Technology
Fluorescence Spectroscopy Studies of Intermolecular Interactions in Supercritical Fluids
Solvation Structures in Supercritical Fluid Mixtures Based on Molecular
Distribution Functions
Gibbs-Ensemble Monte Carlo Simulations of Phase Equilibria in Supercritical
Fluid Mixtures
Spectroscopic Determination of Solvent Strength and Structure in Supercritical
Fluid Mixtures: A Review
Partition Coefficients of Poly(ethylene glycol)s in Supercritical Carbon Dioxide
Experimental Measurement of Supercritical Fluid-Liquid Phase Equilibrium
Vapor-Liquid Equilibria of Fatty Acid Esters in Supercritical Fluids
Four-Phase Equilibrium of Two Ternary Organic Systems with Carbon Dioxide
Direct Viscosity Enhancement of Carbon Dioxide
Pressure Tuning of Reverse Micelles for Adjustable Solvation of Hydrophiles in Supercritical Fluids
Structure of Reverse Micelle and Microemulsion Phases in Near-Critical and
Supercritical Fluid as Determined from Dynamic Light-Scattering Studies
Inverse Emulsion Polymerization of Acrylamide in Near-Critical and Supercritical
Continuous Phases
Interaction of Polymers with Near-Critical Carbon Dioxide
Kinetic Elucidation of the Acid-Catalyzed Mechanism of 1-Propanol Dehydration in
Supercritical Water
Chemistry of Metholynaphthalene in Supercritical Water
Fundamental Kinetics of Methonol Oxidation
Thermodynamic Analysis of Corrosion of Iron Alloys in Supercritical Water
Electrochemical Measurements of Corrosion of Iron Alloys in Supercritical Water
Phase and Reaction Equilibria Considerations in the Evaluation and Operation of
Supercritical Fluid Reaction Processes
Kinetic Model for Supercritical-Delignification of Wood
Gas Antisolvent Recrystallization: New Process to Recrystallize Compounds
Insoluble in Supercritical Fluids
Solids Formation After the Expansion of Supercritical Mixtures
Solid-Fluid Mass Transfer in a Packed Bed Under Supercritical Conditions
Two-Phase Heat Transfer in the Vicinity of a Lower Consolute Point
Extraction and Isolation of Chemotherapeutic Pyrrolizidine Alkaloids from Plant
Substrates: Novel Process Using Supercritical Fluids
Supercritical Fluid Carbon Dioxide Extraction in the Synthesis of
Trieicosapentaenoylglycerol from Fish Oil
Supercritical Carbon Dioxide Extraction of Lipids from Algae
Supercritical Extraction of Pollutants from Water and Soil
Current State of Extraction of Natural Materials with Supercritical Fluids and Developmental Trends
Design, Construction, and Operation of a Multipurpose Plant for Commercial
Supercritical Gas Extraction
Supercritical Fluid Extraction of Flavoring Material: Design and Economics
Selection of Components for Commercial Supercritical Fluid Food Processing
Plants
New Directions in Supercritical Fluid Science and Technology
Fluorescence Spectroscopy Studies of Intermolecular Interactions in Supercritical Fluids
Solvation Structures in Supercritical Fluid Mixtures Based on Molecular
47.

図書
図書
47. A guide to Monte Carlo simulations in statistical physics. 2nd ed (: hard)
David P. Landau, Kurt Binder
出版情報: Cambridge : Cambridge University Press, 2005  xv, 432 p. ; 26 cm
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目次情報: 続きを見る
Introduction / 1：
Some necessary background / 2：
Simple sampling Monte Carlo methods / 3：
Importance sampling Monte Carlo methods / 4：
More on importance sampling Monte Carlo method for lattice systems / 5：
Off-lattice models / 6：
Reweighting methods / 7：
Quantum Monte Carlo methods / 8：
Monte Carlo Renormalization Group methods / 9：
Non-equilibrium and irreversible processes / 10：
Lattice gauge models: a brief introduction / 11：
A brief review of other methods of computer simulation / 12：
Monte Carlo methods outside of physics / 13：
Outlook / 14：
Introduction / 1：
Some necessary background / 2：
Simple sampling Monte Carlo methods / 3：
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