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Maximum Entropy and Bayesian Methods

  • Book
  • © 1990

Overview

Editors:
  1. Paul F. Fougère

    1. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, USA

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Part of the book series: Fundamental Theories of Physics (FTPH, volume 39)

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Table of contents (32 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Probability Theory as Logic

    • E. T. Jaynes
    Pages 1-16

  3. Probability Theory and the Associativity Equation

    • C. Ray Smith, Gary J. Erickson
    Pages 17-30

  4. Objective Bayesianism and Geometry

    • Carlos C. Rodriguez
    Pages 31-39

  5. Consistency Principle for Data-Based Probabilistic Inference

    • V. Solana
    Pages 41-52

  6. An Introduction to Parameter Estimation Using Bayesian Probability Theory

    • G. Larry Bretthorst
    Pages 53-79

  7. From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics

    • T. J. Loredo
    Pages 81-142

  8. Atmospheric 14C Variations: A Bayesian Prospect

    • Charles P. Sonett
    Pages 143-159

  9. On Decoupling Probability from Kinematics in Quantum Mechanics

    • David Hestenes
    Pages 161-183

  10. Bayesian Model Selection and Parameter Estimation Applied to Sea Floor Pressure Data

    • D. Burton, G. J. Moore, W. J. Fitzgerald
    Pages 185-194

  11. Applications of Maximum Entropy and Bayesian Methods in Neutron Scattering

    • Devinderjit Singh Sivia
    Pages 195-209

  12. The Conditional Entropy of a Canonical Constraint

    • Randall Barron
    Pages 211-220

  13. Solving Oversampled Data Problems By Maximum Entropy

    • R. K. Bryan
    Pages 221-232

  14. Constructing Priors in Maximum Entropy Methods

    • N. Rivier, R. Englman, R. D. Levine
    Pages 233-242

  15. Maximum Entropy with Nonlinear Constraints: Physical Examples

    • A. J. M. Garrett
    Pages 243-249

  16. Maximum Entropy Description of Plasma Equilibrium

    • A. J. M. Garrett
    Pages 251-271

  17. Linear Inversion by the Maximum Entropy Method with Specific Non-Trivial Prior Information

    • V. A. Macaulay, B. Buck
    Pages 273-280

  18. Minimum Dissipation And Maximum Entropy

    • David Montgomery, Lee Phillips
    Pages 281-296

  19. Maximum Entropy and Equations of State for Random Cellular Structures

    • N. Rivier
    Pages 297-308

  20. Delay Estimation using Maximum Entropy Derived Phase Information

    • Jacqueline Yao, Nathan Ida, Louis Roemer, Ke-Sheng Huo
    Pages 309-324
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Keywords

About this book

This volume represents the proceedings of the Ninth Annual MaxEnt Workshop, held at Dartmouth College in Hanover, New Hampshire, on August 14-18, 1989. These annual meetings are devoted to the theory and practice of Bayesian Probability and the Maximum Entropy Formalism. The fields of application exemplified at MaxEnt '89 are as diverse as the foundations of probability theory and atmospheric carbon variations, the 1987 Supernova and fundamental quantum mechanics. Subjects include sea floor drug absorption in man, pressures, neutron scattering, plasma equilibrium, nuclear magnetic resonance, radar and astrophysical image reconstruction, mass spectrometry, generalized parameter estimation, delay estimation, pattern recognition, heave responses in underwater sound and many others. The first ten papers are on probability theory, and are grouped together beginning with the most abstract followed by those on applications. The tenth paper involves both Bayesian and MaxEnt methods and serves as a bridge to the remaining papers which are devoted to Maximum Entropy theory and practice. Once again, an attempt has been made to start with the more theoretical papers and to follow them with more and more practical applications. Papers number 29, 30 and 31, by Kesaven, Seth and Kapur, represent a somewhat different, perhaps even "unorthodox" viewpoint, and are included here even though the editor and, indeed many in the audience at Dartmouth, disagreed with their content. I feel that scientific disagreements are essential in any developing field, and often lead to a deeper understanding.

Editors and Affiliations

  • Department of Mathematics, Massachusetts Institute of Technology, Cambridge, USA

    Paul F. Fougère

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