Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction Book

	Introduction	1
Pt. I	Bayesian Image Analysis: Introduction
1	The Bayesian Paradigm	9
2	Cleaning Dirty Pictures	29
3	Finite Random Fields	55
Pt. II	The Gibbs Sampler and Simulated Annealing
4	Markov Chains: Limit Theorems	75
5	Gibbsian Sampling and Annealing	113
6	Cooling Schedules	129
Pt. III	Variations of the Gibbs Sampler
7	Gibbsian Sampling and Annealing Revisited	143
8	Partially Parallel Algorithms	153
9	Synchronous Algorithms	159
Pt. IV	Metropolis Algorithms and Spectral Methods
10	Metropolis Algorithms	179
11	The Spectral Gap and Convergence of Markov Chains	197
12	Eigenvalues, Sampling, Variance Reduction	203
13	Continuous Time Processes	209
Pt. V	Texture Analysis
14	Partitioning	217
15	Random Fields and Texture Models	231
16	Bayesian Texture Classification	243
Pt. VI	Parameter Estimation
17	Maximum Likelihood Estimation	251
18	Consistency of Spatial ML Estimators	263
19	Computation of Full ML Estimators	281
Pt. VII	Supplement
20	A Glance at Neural Networks	301
21	Three Applications	313
Pt. VIII	Appendix
A	Simulation of Random Variables	327
B	Analytical Tools	343
C	Physical Imaging Systems	351
D	The Software Package AntsInFields	355
	References	357
	Symbols	379
	Index	381