Maximum-entropy and Bayesian methods in science and engineering

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書誌事項

Maximum-entropy and Bayesian methods in science and engineering

edited by Gary J. Erickson and C. Ray Smith

（Fundamental theories of physics, v. 31-32）

Kluwer Academic Publishers, c1988

  • : set
  • v. 1
  • v. 2

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注記

v. 1: Foundations. -- v. 2: Applications

内容説明・目次

巻冊次

v. 1 ISBN 9789027727930

内容説明

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 In particular, we are delighted to make available Professor E. T. Jaynes' unpublished Stanford University Microwave Laboratory Report No. 421 "How Does the Brain Do Plausible Reasoning?"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

目次

How does the Brain Do Plausible Reasoning?.- The Relation of Bayesian and Maximum Entropy Methods.- An Engineer Looks at Bayes.- Bayesian Inductive Inference and Maximum Entropy.- Excerpts from Bayesian Spectrum Analysis and Parameter Estimation.- Detection of Extra-Solar System Planets.- Stochastic Complexity and the Maximum Entropy Principle.- The Axioms of Maximum Entropy.- Understanding Ignorance.- Maximum Entropy Calculations on a Discrete Probability Space.- Quantum Density Matrix and Entropic Uncertainty.- Information-Theoretical Generalization of the Uncertainty Principle.- Time, Energy, and the Limits of Measurement.- On a Detection Estimator Related to Entropy.- The Evolution of Carnot’s Principle.- A Logic of Information Systems.- Methodological Principles of Uncertainty in Inductive Modelling: A New Perspective.- Comparison of Minimum Cross-Entropy Inference with Minimally Informative Information Systems.
巻冊次

v. 2 ISBN 9789027727947

内容説明

This volume has its origin in the Fifth, Sixth and Seventh Workshops on "Maximum-Entropy and Bayesian Methods in Applied Statistics", held at 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 most of the papers in this volume are in the nature of advancing theory or solving specific problems, as opposed to status reports, it is believed that the contents 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. These workshops and their proceedings could not have been brought to their final form without the support or help of a number of people.

目次

Seti, Radon Transforms, and Optimization.- Inductive Inference by Neutral Networks.- On the Efficiency of a Class of Maximum Entropy Estimators.- Quantum Statistical Mechanics in Phase Space and the Classical Limit.- Superposition Effects in Differential Entropy and Kullback-Leibler Information.- Super Variational Principles.- Einstein’s Reversal of the Boltzmann Principle and Particle Statistics.- Classical Entropy of a Coherent Spin State: A Local Minimum.- Least Maximum Entropy and Minimum Uncertainty Coherent States.- Maximum Entropy Spectroscopy — Dimes and Mesa.- Information and Entropy of Patterns in Genetic Switchs.- Maximum Entropy and the Phase Problem in Protein Crystallography.- Contrast Transfer Function Correction in Electron Microscopy.- Climatically Induced Cyclic Variations in United States Crop Production: Implications in Economic and Social Science.- A Maximum Entropy Method for Expert System Construction.- Stochastic Relaxation Methods for Image Restoration and Expert Systems.- Towards a Method of Correcting Bias Introduced By the Use of the Error Fitting Method in Conjunction with a Maximum Entropy Processing Algorithm.- Making Maximum Entropy Computations Easier by Adding Extra Constraints (Extended Abstract).- Image Restoration and Reconstruction Using Entropy as a Regularization Functional.- Application of Likelihood And Entropy for Toeplitz Constrained Covariance Estimation.- The Concept of Epoch Entropy in Complex Systems.- A General Theory of Inhomogeneous Systems.- Maximum Entropy and Crack Geometry in Granitic Rocks.- Maximum Entropy Analysis of Liquid Diffraction Data.- Random Array Beamforming.- Decision Making with Barely Any Information: The Role of Mixed Strategies.- Comparison of Bayesian and Dempster’s Rules In EvidenceCombination.

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