Winkler G. Image Analysis, Random Fields...Markov Chain...2ed 2012 torrent

Textbook in PDF format

This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required.
The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added.
Preface to the Second Edition
Preface to the First Edition
Introduction
Bayesian Image Analysis: Introduction
The Bayesian Paradigm
Cleaning Dirty Pictures
Finite Random Fields
The Gibbs Sampler and Simulated Annealing
Markov Chains: Limit Theorems
Gibbsian Sampling and Annealing
Cooling Schedules
Variations of the Gibbs Sampler
Gibbsian Sampling and Annealing Revisited
Partially Parallel Algorithms
Synchronous Algorithms
Metropolis Algorithms and Spectral Methods
Metropolis Algorithms
The Spectral Gap and Convergence of Markov Chains
Eigenvalues, Sampling, Variance Reduction
Continuous Time Processes
Texture Analysis
Partitioning
Random Fields and Texture Models
Bayesian Texture Classification
Parameter Estimation
Maximum Likelihood Estimation
Consistency of Spatial ML Estimators
Computation of Full ML Estimators
Supplement
A Glance at Neural Networks
Three Applications
Appendix
Simulation of Random Variables
Analytical Tools
Physical Imaging Systems
The Software Package AntsInFields
References
Symbols
Index