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E. T. Jaynes: Papers on Probability, Statistics and Statistical Physics

  • Book
  • © 1989

Overview

Editors:
  1. R. D. Rosenkrantz

    1. Department of Mathematics, Dartmouth College, USA

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Part of the book series: Synthese Library (SYLI, volume 158)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xxiv
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  2. Introductory Remarks

    • R. D. Rosenkrantz
    Pages 1-3

  3. Information Theory and Statistical Mechanics I (1957)

    • R. D. Rosenkrantz
    Pages 4-16

  4. Information Theory and Statistical Mechanics II (1957)

    • R. D. Rosenkrantz
    Pages 17-38

  5. Brandeis Lectures (1963)

    • R. D. Rosenkrantz
    Pages 39-76

  6. Gibbs vs Boltzmann Entropies (1965)

    • R. D. Rosenkrantz
    Pages 77-86

  7. Delaware Lecture (1967)

    • R. D. Rosenkrantz
    Pages 87-113

  8. Prior Probabilities (1968)

    • R. D. Rosenkrantz
    Pages 114-130

  9. The Well-Posed Problem (1973)

    • R. D. Rosenkrantz
    Pages 131-148

  10. Confidence Intervals vs Bayesian Intervals (1976)

    • R. D. Rosenkrantz
    Pages 149-209

  11. Where Do We Stand on Maximum Entropy? (1978)

    • R. D. Rosenkrantz
    Pages 210-314

  12. Concentration of Distributions at Entropy Maxima (1979)

    • R. D. Rosenkrantz
    Pages 315-336

  13. Marginalization and Prior Probabilities (1980)

    • R. D. Rosenkrantz
    Pages 337-375

  14. What is the Question? (1981)

    • R. D. Rosenkrantz
    Pages 376-400

  15. The Minimum Entropy Production Principle (1980)

    • R. D. Rosenkrantz
    Pages 401-424

  16. Back Matter

    Pages 425-434
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Keywords

About this book

The first six chapters of this volume present the author's 'predictive' or information theoretic' approach to statistical mechanics, in which the basic probability distributions over microstates are obtained as distributions of maximum entropy (Le. , as distributions that are most non-committal with regard to missing information among all those satisfying the macroscopically given constraints). There is then no need to make additional assumptions of ergodicity or metric transitivity; the theory proceeds entirely by inference from macroscopic measurements and the underlying dynamical assumptions. Moreover, the method of maximizing the entropy is completely general and applies, in particular, to irreversible processes as well as to reversible ones. The next three chapters provide a broader framework - at once Bayesian and objective - for maximum entropy inference. The basic principles of inference, including the usual axioms of probability, are seen to rest on nothing more than requirements of consistency, above all, the requirement that in two problems where we have the same information we must assign the same probabilities. Thus, statistical mechanics is viewed as a branch of a general theory of inference, and the latter as an extension of the ordinary logic of consistency. Those who are familiar with the literature of statistics and statistical mechanics will recognize in both of these steps a genuine 'scientific revolution' - a complete reversal of earlier conceptions - and one of no small significance.

Editors and Affiliations

  • Department of Mathematics, Dartmouth College, USA

    R. D. Rosenkrantz

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