Image Analysis, Random Fields and Dynamic Monte Carlo Methods

The text presents Bayesian image analysis and dynamic Monte Carlo algorithms from the mathematical point of view. The subject is introduced at a moderate pace and the proofs are thorough. Specific models are developed step by step and discussed.

Inhalt
I. Bayesian Image Analysis: Introduction.- 1. The Bayesian Paradigm.- 2. Cleaning Dirty Pictures.- 3. Random Fields.- II. The Gibbs Sampler and Simulated Annealing.- 4. Markov Chains: Limit Theorems.- 5. Sampling and Annealing.- 6. Cooling Schedules.- 7. Sampling and Annealing Revisited.- III. More on Sampling and Annealing.- 8. Metropolis Algorithms.- 9. Alternative Approaches.- 10. Parallel Algorithms.- IV. Texture Analysis.- 11. Partitioning.- 12. Texture Models and Classification.- V. Parameter Estimation.- 13. Maximum Likelihood Estimators.- 14. Spacial ML Estimation.- VI. Supplement.- 15. A Glance at Neural Networks.- 16. Mixed Applications.- VII. Appendix.- A. Simulation of Random Variables.- B. The Perron-Frobenius Theorem.- C. Concave Functions.- D. A Global Convergence Theorem for Descent Algorithms.- References.