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1.

図書
図書
1. 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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2.

図書
図書
2. 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：
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