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1.

図書

図書
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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2.

図書

図書
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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3.

図書

図書
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:
4.

図書

図書
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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5.

図書

図書
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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6.

図書

図書
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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7.

図書

図書
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
8.

図書

図書
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:
9.

図書

図書
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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10.

図書

図書
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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目次情報: 続きを見る
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
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