Maximum-Entropy and Bayesian Methods in Science and Engineering

Foundations

  • Gary J. Erickson
  • C. Ray Smith

Part of the Fundamental Theories of Physics book series (FTPH, volume 31-32)

Table of contents

  1. Front Matter
    Pages i-x
  2. Myron Tribus
    Pages 31-52
  3. E. T. Jaynes
    Pages 147-160
  4. John Skilling
    Pages 173-187
  5. C. C. Rodriguez
    Pages 189-204
  6. R. Blankenbecler, M. H. Partovi
    Pages 235-244
  7. M. H. Partovi, R. Blankenbecler
    Pages 249-255
  8. E. T. Jaynes
    Pages 267-281
  9. N. C. Dalkey
    Pages 283-294
  10. Back Matter
    Pages 313-314

About this book

Introduction

This volume has its origin in the Fifth, Sixth and Seventh Workshops on and Bayesian Methods in Applied Statistics", held at "Maximum-Entropy the University of Wyoming, August 5-8, 1985, and at Seattle University, August 5-8, 1986, and August 4-7, 1987. It was anticipated that the proceedings of these workshops would be combined, so most of the papers were not collected until after the seventh workshop. Because all of the papers in this volume are on foundations, it is believed that the con­ tents of this volume will be of lasting interest to the Bayesian community. The workshop was organized to bring together researchers from different fields to critically examine maximum-entropy and Bayesian methods in science and engineering as well as other disciplines. Some of the papers were chosen specifically to kindle interest in new areas that may offer new tools or insight to the reader or to stimulate work on pressing problems that appear to be ideally suited to the maximum-entropy or Bayesian method. A few papers presented at the workshops are not included in these proceedings, but a number of additional papers not presented at the workshop are included. In particular, we are delighted to make available Professor E. T. Jaynes' unpublished Stanford University Microwave Laboratory Report No. 421 "How Does the Brain Do Plausible Reasoning?" (dated August 1957). This is a beautiful, detailed tutorial on the Cox-Polya-Jaynes approach to Bayesian probability theory and the maximum-entropy principle.

Keywords

Estimator Information Maximum entropy method Probability space complexity entropy logic

Editors and affiliations

  • Gary J. Erickson
    • 1
  • C. Ray Smith
    • 2
  1. 1.Department of Electrical EngineeringSeattle UniversitySeattleUSA
  2. 2.Advanced Sensors Directorate Research, Development and Engineering CenterUS Army Missile CommandRedstone ArsenalUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-009-3049-0
  • Copyright Information Springer Science+Business Media B.V. 1988
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-7871-9
  • Online ISBN 978-94-009-3049-0
  • About this book
Industry Sectors
Pharma
Biotechnology
Telecommunications
Consumer Packaged Goods
Aerospace
Oil, Gas & Geosciences