Image Analysis, Random Fields, and Dynamic Monte Carlo Methods: A Mathematical Introduction

Front Matter....Pages I-XVI
Introduction....Pages 1-5
Front Matter....Pages 7-7
The Bayesian Paradigm....Pages 9-28
Cleaning Dirty Pictures....Pages 29-53
Finite Random Fields....Pages 55-72
Front Matter....Pages 73-73
Markov Chains: Limit Theorems....Pages 75-112
Gibbsian Sampling and Annealing....Pages 113-128
Cooling Schedules....Pages 129-140
Front Matter....Pages 141-141
Gibbsian Sampling and Annealing Revisited....Pages 143-151
Partially Parallel Algorithms....Pages 153-158
Synchronous Algorithms....Pages 159-175
Front Matter....Pages 177-177
Metropolis Algorithms....Pages 179-196
The Spectral Gap and Convergence of Markov Chains....Pages 197-202
Eigenvalues, Sampling, Variance Reduction....Pages 203-207
Continuous Time Processes....Pages 209-213
Front Matter....Pages 215-215
Partitioning....Pages 217-229
Random Fields and Texture Models....Pages 231-242
Bayesian Texture Classification....Pages 243-248
Front Matter....Pages 249-249
Maximum Likelihood Estimation....Pages 251-261
Consistency of Spatial ML Estimators....Pages 263-280
Computation of Full ML Estimators....Pages 281-298
Front Matter....Pages 299-299
A Glance at Neural Networks....Pages 301-311
Three Applications....Pages 313-323
Back Matter....Pages 325-387