Maximum Entropy and Bayesian Methods: Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical AnalysisThis volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 -8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (point-spread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people. |
Contents
CVNP BAYESIANISM BY MCMC | 15 |
A COMPARISON | 35 |
PROBABILISTIC METHODS FOR DATA FUSION | 57 |
WHENCE THE LAWS OF PROBABILITY? | 71 |
BAYESIAN GROUP ANALYSIS | 87 |
SYMMETRYGROUP JUSTIFICATION OF MAXIMUM ENTROPY METHOD | 101 |
PROBABILITY SYNTHESIS HOW TO EXPRESS PROBABILITIES IN TERMS | 115 |
MODEL COMPARISON WITH ENERGY CONFINEMENT DATA FROM LARGE | 137 |
AN EMPIRICAL MODEL OF BRAIN SHAPE | 199 |
DIFFICULTIES APPLYING BLIND SOURCE SEPARATION TECHNIQUES | 209 |
THE HISTORY OF PROBABILITY THEORY | 223 |
LEVINLIVITÁNYI | 239 |
ASTROPHYSICAL | 253 |
COMPUTATIONAL EXPLORATION OF THE ENTROPIC PRIOR OVEK SPACES | 263 |
ENVIRONMENTALLYORIENTED PROCESSING OF MULTISPECTRAL | 271 |
MAXIMUM ENTROPY APPROACH TO OPTIMAL SENSOR PLACEMENT | 277 |
A BAYESIAN APPROACH FOR THE DETERMINATION OF THE CHARGE | 153 |
INTEGRATED DEFORMABLE BOUNDARY FINDING USING BAYESIAN | 171 |
SHAPE RECONSTRUCTION IN XRAY TOMOGRAPHY FROM A SMALL | 183 |
MAXIMUM ENTROPY UNDER UNCERTAINTY | 291 |
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acausal processes adjoint algorithm analysis applications arg max assign assume assumptions Bayes Bayesian approach Bayesian Methods boundary finding calculation Charge density computational constraints covariance criterion data fusion defined derived described differentiable dỡ Dordrecht entropic prior Entropy and Bayesian equation estimate example expectation values finite formalism Gaussian geometric given gradient hyperparameters IEEE Image Processing integral inverse problem ISBN iteration Jaynes K. M. Hanson Kluwer Kolmogorov complexity Kreinovich likelihood function linear logical Markov MassInf mathematical matrix MaxEnt Maximum Entropy Maximum Entropy method maximum likelihood mean measure minterm Mohammad-Djafari noise objective obtained optimal sensor placement parameters physics possible posterior posterior probability prior Bayesian prior probability probability distribution probability theory product rule propositions Quantum real number region samples shape signals simulated solution solve statistics symmetry techniques theorem transformation uncertainty Unmix utility function variables vector waveforms