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Maximum Entropy and Bayesian Methods

Seattle, 1991

  • Book
  • © 1992

Overview

Editors:
  1. C. Ray Smith

    1. Research, Development and Engineering Center, U.S. Missile Command, Redstone, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Gary J. Erickson

    1. Department of Electrical Engineering, Seattle University, Seattle, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Paul O. Neudorfer

    1. Department of Electrical Engineering, Seattle University, Seattle, USA

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Part of the book series: Fundamental Theories of Physics (FTPH, volume 50)

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Table of contents (35 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. The Gibbs Paradox

    • E. T. Jaynes
    Pages 1-21

  3. Bayesian Solution of Ordinary Differential Equations

    • John Skilling
    Pages 23-37

  4. Bayesian Interpolation

    • David J. C. MacKay
    Pages 39-66

  5. Estimating the Ratio of Two Amplitudes in Nuclear Magnetic Resonance Data

    • G. Larry Bretthorst
    Pages 67-77

  6. A Bayesian Method for the Detection of a Periodic Signal of Unknown Shape and Period

    • P. C. Gregory, T. J. Loredo
    Pages 79-103

  7. Linking the Plausible and Demonstrative Inferences

    • Vicente Solana
    Pages 105-120

  8. Dimensional Analysis in Data Modelling

    • G. A. Vignaux
    Pages 121-126

  9. Entropies of Likelihood Functions

    • Michael Hardy
    Pages 127-130

  10. Maximum Likelihood Estimation of the Lagrange Parameters of the Maximum Entropy Distributions

    • Ali Mohammad-Djafari
    Pages 131-139

  11. Entropy of Form and Hierarchic Organization

    • J. Wagensberg, R. Pastor
    Pages 141-151

  12. A Bayesian Looks at the Anthropic Principle

    • A. J. M. Garrett
    Pages 153-164

  13. The Evidence for Neural Networks

    • David J. C. MacKay
    Pages 165-183

  14. Unmixing Mineral Spectra Using a Neural Net with Maximum Entropy Regularization

    • Neil Pendock
    Pages 185-196

  15. Bayesian Mixture Modeling

    • Radford M. Neal
    Pages 197-211

  16. Point-Process Theory and the Surveillance of Many Objects

    • John M. Richardson, Kenneth A. Marsh
    Pages 213-220

  17. A Matlab Program to Calculate the Maximum Entropy Distributions

    • Ali Mohammad-Djafari
    Pages 221-233

  18. Memsys as Debugger

    • Tj. Romke Bontekoe, Do Kester
    Pages 235-240

  19. Entropy and Sunspots: Their Bearing on Time-Series

    • Brian Buck, Vincent A. Macaulay
    Pages 241-252

  20. Basic Concepts in Multisensor Data Fusion

    • John M. Richardson, Kenneth A. Marsh
    Pages 253-271
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Keywords

About this book

Bayesian probability theory and maximum entropy methods are at the core of a new view of scientific inference. These `new' ideas, along with the revolution in computational methods afforded by modern computers, allow astronomers, electrical engineers, image processors of any type, NMR chemists and physicists, and anyone at all who has to deal with incomplete and noisy data, to take advantage of methods that, in the past, have been applied only in some areas of theoretical physics.
This volume records the Proceedings of Eleventh Annual `Maximum Entropy' Workshop, held at Seattle University in June, 1991. These workshops have been the focus of a group of researchers from many different fields, and this diversity is evident in this volume. There are tutorial papers, theoretical papers, and applications in a very wide variety of fields. Almost any instance of dealing with incomplete and noisy data can be usefully treated by these methods, and many areas of theoretical research are being enhanced by the thoughtful application of Bayes' theorem. The contributions contained in this volume present a state-of-the-art review that will be influential and useful for many years to come.

Editors and Affiliations

  • Research, Development and Engineering Center, U.S. Missile Command, Redstone, USA

    C. Ray Smith

  • Department of Electrical Engineering, Seattle University, Seattle, USA

    Gary J. Erickson, Paul O. Neudorfer

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