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