Maximum Entropy and Bayesian Methods

1. Front Matter

   Pages i-ix

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2. Massive Inference and Maximum Entropy

   • John Skilling

   Pages 1-14

3. CV-NP Bayesianism by MCMC

   • Carlos C. Rodriguez

   Pages 15-24

4. Which Algorithms are Feasible? Maxent Approach

   • D. E. Cooke, V. Kreinovich, L. Longpré

   Pages 25-33

5. Maximum Entropy, Likelihood and Uncertainty: A Comparison

   • A. Golan

   Pages 35-56

6. Probabilistic Methods for Data Fusion

   • Ali Mohammad-Djafari

   Pages 57-69

7. Whence the Laws of Probability?

   • Anthony J. M. Garrett

   Pages 71-86

8. Bayesian Group Analysis

   • W. Von Der Linden, V. Dose, A. Ramaswami

   Pages 87-99

9. Symmetry-Group Justification of Maximum Entropy Method and Generalized Maximum Entropy Methods in Image Processing

   • O. Kosheleva

   Pages 101-113

10. Probability Synthesis

    • Anthony J. M. Garrett

    Pages 115-120

11. Inversion Based on Computational Simulations

    • K. M. Hanson, G. S. Cunningham, S. S. Saquib

    Pages 121-135

12. Model Comparison with Energy Confinement Data from Large Fusion Experiments

    • R. Preuss, V. Dose, W. Von Der Linden

    Pages 137-145

13. Deconvolution Based on Experimentally Determined Apparatus Functions

    • V. Dose, R. Fischer, W. von der Linden

    Pages 147-152

14. A Bayesian Approach for the Determination of the Charge Density from Elastic Electron Scattering Data

    • A. Mohammad-Djafari, H. G. Miller

    Pages 153-170

15. Integrated Deformable Boundary Finding Using Bayesian Strategies

    • Amit Chakraborty, James S. Duncan

    Pages 171-182

16. Shape Reconstruction in X-Ray Tomography from a Small Number of Projections Using Deformable Models

    • Ali Mohammad-Djafari, Ken Sauer

    Pages 183-198

17. An Empirical Model of Brain Shape

    • J. C. Gee, L. Le Briquer

    Pages 199-207

18. Difficulties Applying Recent Blind Source Separation Techniques to EEG and MEG

    • K. H. Knuth

    Pages 209-222

19. The History of Probability Theory

    • Anthony J. M. Garrett

    Pages 223-238

20. We must Choose the Simplest Physical Theory: Levin-Li-Vitányi Theorem and its Potential Physical Applications

    • D. Fox, M. Schmidt, M. Koshelev, V. Kreinovich, L. Longpré, J. Kuhn

    Pages 239-251

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.

• Experiment
• Maximum entropy method
• Potential
• Probability theory
• algorithms
• bayesian statistics
• best fit
• coding theory
• image processing
• information
• information theory
• machine learning
• statistics
• uncertainty

• Department of Electronical Engineering, Boise State University, Boise, USA

  Gary J. Erickson, Joshua T. Rychert

• Fayetteville, USA

  C. Ray Smith

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