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

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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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.

• probability
• statistical physics
• statistics

• Department of Mathematics, Dartmouth College, USA

  R. D. Rosenkrantz

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