Maximum Entropy and Bayesian Methods

Cambridge, England, 1994 Proceedings of the Fourteenth International Workshop on Maximum Entropy and Bayesian Methods

  • John Skilling
  • Sibusiso Sibisi
Conference proceedings

Part of the Fundamental Theories of Physics book series (FTPH, volume 70)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Applications

    1. E. J. Fordham, D. Xing, J. A. Derbyshire, S. J. Gibbs, T. A. Carpenter, L. D. Hall
      Pages 1-12
    2. G. J. Marseille, R. de Beer, M. Fuderer, A. F. Mehlkopf, D. van Ormondt
      Pages 13-22
    3. S. M. Glidewell, B. A. Goodman, J. Skilling
      Pages 23-30
    4. W. von der Linden, K. Ertl, V. Dose
      Pages 41-49
    5. N. J. Davidson, B. J. Cole, H. G. Miller
      Pages 51-58
    6. Vincent A. Macaulay, Brian Buck
      Pages 59-67
    7. Steen Hansen, Jürgen J. Müller
      Pages 69-78
    8. Li-He Zou, Zhengrong Wang, Louis E. Roemer
      Pages 79-89
    9. F. Solms, P. G. W. van Rooyen, J. S. Kunicki
      Pages 101-108
    10. L. Stergioulas, A. Vourdas, G. R. Jones
      Pages 109-116
  3. Algorithms

    1. John Stutz, Peter Cheeseman
      Pages 117-126
    2. Paul Desmedt, Ignace Lemahieu, K. Thielemans
      Pages 127-134
    3. Myron Tribus
      Pages 143-155
    4. Kenneth M. Hanson, Gregory S. Cunningham
      Pages 157-164
    5. Anthony J. M. Garrett
      Pages 165-174
    6. C. C. Rodríguez
      Pages 175-182

About these proceedings

Introduction

This volume records papers given at the fourteenth international maximum entropy conference, held at St John's College Cambridge, England. It seems hard to believe that just thirteen years have passed since the first in the series, held at the University of Wyoming in 1981, and six years have passed since the meeting last took place here in Cambridge. So much has happened. There are two major themes at these meetings, inference and physics. The inference work uses the confluence of Bayesian and maximum entropy ideas to develop and explore a wide range of scientific applications, mostly concerning data analysis in one form or another. The physics work uses maximum entropy ideas to explore the thermodynamic world of macroscopic phenomena. Of the two, physics has the deeper historical roots, and much of the inspiration behind the inference work derives from physics. Yet it is no accident that most of the papers at these meetings are on the inference side. To develop new physics, one must use one's brains alone. To develop inference, computers are used as well, so that the stunning advances in computational power render the field open to rapid advance. Indeed, we have seen a revolution. In the larger world of statistics beyond the maximum entropy movement as such, there is now an explosion of work in Bayesian methods, as the inherent superiority of a defensible and consistent logical structure becomes increasingly apparent in practice.

Keywords

Markov model Probability theory classification image processing maximum entropy method neural networks thermodynamics

Editors and affiliations

  • John Skilling
    • 1
  • Sibusiso Sibisi
    • 1
  1. 1.Department of Applied Mathematics and Theoretical PhysicsUniversity of CambridgeCambridgeEngland

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-009-0107-0
  • Copyright Information Springer Science+Business Media B.V. 1996
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-6534-4
  • Online ISBN 978-94-009-0107-0
  • Buy this book on publisher's site