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Maximum-Entropy and Bayesian Methods in Science and Engineering: Foundations

✍ Scribed by E. T. Jaynes (auth.), Gary J. Erickson, C. Ray Smith (eds.)


Publisher
Springer Netherlands
Year
1988
Tongue
English
Leaves
320
Series
Fundamental Theories of Physics 31-32
Edition
1
Category
Library

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✦ Synopsis


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 conΒ­ 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 In particular, we are delighted to make available Professor E. T. Jaynes' unpublished Stanford University Microwave Laboratory Report No. 421 "How Does the Brain Do Plausible Reasoning?"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.

✦ Table of Contents


Front Matter....Pages i-x
How Does the Brain Do Plausible Reasoning?....Pages 1-24
The Relation of Bayesian and Maximum Entropy Methods....Pages 25-29
An Engineer Looks at Bayes....Pages 31-52
Bayesian Inductive Inference and Maximum Entropy....Pages 53-74
Excerpts from Bayesian Spectrum Analysis and Parameter Estimation....Pages 75-145
Detection of Extra-Solar System Planets....Pages 147-160
Stochastic Complexity and the Maximum Entropy Principle....Pages 161-171
The Axioms of Maximum Entropy....Pages 173-187
Understanding Ignorance....Pages 189-204
Maximum Entropy Calculations on a Discrete Probability Space....Pages 205-234
Quantum Density Matrix and Entropic Uncertainty....Pages 235-244
Information-Theoretical Generalization of the Uncertainty Principle....Pages 245-248
Time, Energy, and the Limits of Measurement....Pages 249-255
On a Detection Estimator Related to Entropy....Pages 257-265
The Evolution of Carnot’s Principle....Pages 267-281
A Logic of Information Systems....Pages 283-294
Methodological Principles of Uncertainty in Inductive Modelling: A New Perspective....Pages 295-304
Comparison of Minimum Cross-Entropy Inference with Minimally Informative Information Systems....Pages 305-312
Back Matter....Pages 313-314

✦ Subjects


Statistics, general;Signal, Image and Speech Processing;Theoretical, Mathematical and Computational Physics;Electrical Engineering


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