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Maximum Entropy and Bayesian Methods

✍ Scribed by E. T. Jaynes (auth.), Paul F. Fougère (eds.)


Publisher
Springer Netherlands
Year
1990
Tongue
English
Leaves
480
Series
Fundamental Theories of Physics 39
Edition
1
Category
Library

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✦ Synopsis


This volume represents the proceedings of the Ninth Annual MaxEnt Workshop, held at Dartmouth College in Hanover, New Hampshire, on August 14-18, 1989. These annual meetings are devoted to the theory and practice of Bayesian Probability and the Maximum Entropy Formalism. The fields of application exemplified at MaxEnt '89 are as diverse as the foundations of probability theory and atmospheric carbon variations, the 1987 Supernova and fundamental quantum mechanics. Subjects include sea floor drug absorption in man, pressures, neutron scattering, plasma equilibrium, nuclear magnetic resonance, radar and astrophysical image reconstruction, mass spectrometry, generalized parameter estimation, delay estimation, pattern recognition, heave responses in underwater sound and many others. The first ten papers are on probability theory, and are grouped together beginning with the most abstract followed by those on applications. The tenth paper involves both Bayesian and MaxEnt methods and serves as a bridge to the remaining papers which are devoted to Maximum Entropy theory and practice. Once again, an attempt has been made to start with the more theoretical papers and to follow them with more and more practical applications. Papers number 29, 30 and 31, by Kesaven, Seth and Kapur, represent a somewhat different, perhaps even "unorthodox" viewpoint, and are included here even though the editor and, indeed many in the audience at Dartmouth, disagreed with their content. I feel that scientific disagreements are essential in any developing field, and often lead to a deeper understanding.

✦ Table of Contents


Front Matter....Pages i-xiii
Probability Theory as Logic....Pages 1-16
Probability Theory and the Associativity Equation....Pages 17-30
Objective Bayesianism and Geometry....Pages 31-39
Consistency Principle for Data-Based Probabilistic Inference....Pages 41-52
An Introduction to Parameter Estimation Using Bayesian Probability Theory....Pages 53-79
From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics....Pages 81-142
Atmospheric 14 C Variations: A Bayesian Prospect....Pages 143-159
On Decoupling Probability from Kinematics in Quantum Mechanics....Pages 161-183
Bayesian Model Selection and Parameter Estimation Applied to Sea Floor Pressure Data....Pages 185-194
Applications of Maximum Entropy and Bayesian Methods in Neutron Scattering....Pages 195-209
The Conditional Entropy of a Canonical Constraint....Pages 211-220
Solving Oversampled Data Problems By Maximum Entropy....Pages 221-232
Constructing Priors in Maximum Entropy Methods....Pages 233-242
Maximum Entropy with Nonlinear Constraints: Physical Examples....Pages 243-249
Maximum Entropy Description of Plasma Equilibrium....Pages 251-271
Linear Inversion by the Maximum Entropy Method with Specific Non-Trivial Prior Information....Pages 273-280
Minimum Dissipation And Maximum Entropy....Pages 281-296
Maximum Entropy and Equations of State for Random Cellular Structures....Pages 297-308
Delay Estimation using Maximum Entropy Derived Phase Information....Pages 309-324
Drug Absorption in Man, and its Measurement by MaxEnt....Pages 325-339
Quantified Maximum Entropy....Pages 341-350
Quantified Maxent: An NMR Application....Pages 351-358
Maximum Entropy Reconstruction of Radar Cross-Section Images....Pages 359-368
FIER: A Filtered Entropy Approach to Maxium Entropy Image Restoration....Pages 369-382
Regularisation in Coded-Aperture Spect....Pages 383-389
Burg Algorithm Applied to Fourier Transform ION Cyclotron Resonance Mass Spectrometry....Pages 391-401
Maximum Entropy Analysis for Pattern Recognition....Pages 403-408
Application of Burg’s Spectral Analysis to Heave Response Modeling in Underwater Applications....Pages 409-417
Maximum Entropy and Minimum Cross-Entropy Principles: Need for a Broader Perspective....Pages 419-432
The Inverse MaxEnt and MinxEnt Principles and their Applications....Pages 433-450
A Comprative Assessment of Entropic and Non-Entropic Methods of Estimation....Pages 451-462
Maxent Applied To Linear Regression....Pages 463-473
Back Matter....Pages 475-481

✦ Subjects


Probability Theory and Stochastic Processes;Electrical Engineering;Measurement Science and Instrumentation;Physical Chemistry


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