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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

  • Conference proceedings
  • © 1998

Overview

Editors:
  1. Gary J. Erickson

    1. Department of Electronical Engineering, Boise State University, Boise, USA

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  2. Joshua T. Rychert

    1. Department of Electronical Engineering, Boise State University, Boise, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. C. Ray Smith

    1. Fayetteville, USA

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Part of the book series: Fundamental Theories of Physics (FTPH, volume 98)

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Table of contents (24 papers)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  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
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Keywords

About this book

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.

Editors and Affiliations

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

    Gary J. Erickson, Joshua T. Rychert

  • Fayetteville, USA

    C. Ray Smith

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