Maximum Entropy And Bayesian Methods Boise, Idaho, Usa, 1997 Book

	In Memory of Edwin T. Jaynes
	Preface
	Massive Inference and Maximum Entropy	1
	CV-NP Bayesianism by MCMC	15
	Which Algorithms are Feasible? A Maxent Approach	25
	Maximum Entropy, Likelihood and Uncertainty: A Comparison	35
	Probabilistic Methods for Data Fusion	57
	Whence the Laws of Probability?	71
	Bayesian Group Analysis	87
	Symmetry-Group Justification of Maximum Entropy Method and Generalized Maximum Entropy Methods in Image Processing	101
	Probability Synthesis, how to Express Probabilities in Terms of each other	115
	Inversion Based on Computational Simulations	121
	Model Comparison with Energy Confinement Data from Large Fusion Experiments	137
	Deconvolution Based on Experimentally Determined Apparatus Functions	147
	A Bayesian Approach for the Determination of the Charge Density from Elastic Electron Scattering Data	153
	Integrated Deformable Boundary Finding using Bayesian Strategies	171
	Shape Reconstruction in X-Ray Tomography from a Small Number of Projections using Deformable Models	183
	An Empirical Model of Brain Shape	199
	Difficulties Applying Blind Source Separation Techniques to EEG and MEG	209
	The History of Probability Theory	223
	We must Choose the Simplest Physical Theory: Levin-Li-Vitanyi Theorem and its Potential Physical Applications	239
	Maximum Entropy and Acausal Processes: Astrophysical Applications and Challenges	253
	Computational Exploration of the Entropic Prior over Spaces of Low Dimensionality	263
	Environmentally-Oriented Processing of Multi-Spectral Satellite Images: New Challenges for Bayesian Methods	271
	Maximum Entropy Approach to Optimal Sensor Placement for Aerospace Non-Destructive Testing	277
	Maximum Entropy under Uncertainty	291
	Subject Index	296