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Maximum Entropy and Bayesian Methods: Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis

✍ Scribed by John Skilling (auth.), Gary J. Erickson, Joshua T. Rychert, C. Ray Smith (eds.)


Publisher
Springer Netherlands
Year
1998
Tongue
English
Leaves
299
Series
Fundamental Theories of Physics 98
Edition
1
Category
Library

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✦ Synopsis


This 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.

✦ Table of Contents


Front Matter....Pages i-ix
Massive Inference and Maximum Entropy....Pages 1-14
CV-NP Bayesianism by MCMC....Pages 15-24
Which Algorithms are Feasible? Maxent Approach....Pages 25-33
Maximum Entropy, Likelihood and Uncertainty: A Comparison....Pages 35-56
Probabilistic Methods for Data Fusion....Pages 57-69
Whence the Laws of Probability?....Pages 71-86
Bayesian Group Analysis....Pages 87-99
Symmetry-Group Justification of Maximum Entropy Method and Generalized Maximum Entropy Methods in Image Processing....Pages 101-113
Probability Synthesis....Pages 115-120
Inversion Based on Computational Simulations....Pages 121-135
Model Comparison with Energy Confinement Data from Large Fusion Experiments....Pages 137-145
Deconvolution Based on Experimentally Determined Apparatus Functions....Pages 147-152
A Bayesian Approach for the Determination of the Charge Density from Elastic Electron Scattering Data....Pages 153-170
Integrated Deformable Boundary Finding Using Bayesian Strategies....Pages 171-182
Shape Reconstruction in X-Ray Tomography from a Small Number of Projections Using Deformable Models....Pages 183-198
An Empirical Model of Brain Shape....Pages 199-207
Difficulties Applying Recent Blind Source Separation Techniques to EEG and MEG....Pages 209-222
The History of Probability Theory....Pages 223-238
We must Choose the Simplest Physical Theory: Levin-Li-VitΓ‘nyi Theorem and its Potential Physical Applications....Pages 239-251
Maximum Entropy and Acausal Processes: Astrophysical Applications and Challenges....Pages 253-262
Computational Exploration of the Entropic Prior Over Spaces of Low Dimensionality....Pages 263-269
Environmentally-Oriented Processing of Multi-Spectral Satellite Images: New Challenges for Bayesian Methods....Pages 271-276
Maximum Entropy Approach to Optimal Sensor Placement for Aerospace Non-Destructive Testing....Pages 277-289
Maximum Entropy Under Uncertainty....Pages 291-294
Back Matter....Pages 295-302

✦ Subjects


Probability Theory and Stochastic Processes; Statistics, general; Discrete Mathematics in Computer Science; Coding and Information Theory; Artificial Intelligence (incl. Robotics)


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