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Maximum-Entropy and Bayesian Methods in Science and Engineering

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Cover of 'Maximum-Entropy and Bayesian Methods in Science and Engineering'

Table of Contents

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    Book Overview
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    Chapter 1 How Does the Brain Do Plausible Reasoning?
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    Chapter 2 The Relation of Bayesian and Maximum Entropy Methods
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    Chapter 3 An Engineer Looks at Bayes
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    Chapter 4 Bayesian Inductive Inference and Maximum Entropy
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    Chapter 5 Excerpts from Bayesian Spectrum Analysis and Parameter Estimation
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    Chapter 6 Detection of Extra-Solar System Planets
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    Chapter 7 Stochastic Complexity and the Maximum Entropy Principle
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    Chapter 8 The Axioms of Maximum Entropy
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    Chapter 9 Understanding Ignorance
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    Chapter 10 Maximum Entropy Calculations on a Discrete Probability Space
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    Chapter 11 Quantum Density Matrix and Entropic Uncertainty
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    Chapter 12 Information-Theoretical Generalization of the Uncertainty Principle
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    Chapter 13 Time, Energy, and the Limits of Measurement
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    Chapter 14 On a Detection Estimator Related to Entropy
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    Chapter 15 The Evolution of Carnot’s Principle
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    Chapter 16 A Logic of Information Systems
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    Chapter 17 Methodological Principles of Uncertainty in Inductive Modelling: A New Perspective
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    Chapter 18 Comparison of Minimum Cross-Entropy Inference with Minimally Informative Information Systems
Attention for Chapter 1: How Does the Brain Do Plausible Reasoning?
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