Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Table of Contents

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   Book Overview
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   Chapter 1 Quantum Phases in Entropic Dynamics
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   Chapter 2 Bayesian Approach to Variable Splitting Forward Models
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   Chapter 3 Prior Shift Using the Ratio Estimator
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   Chapter 4 Bayesian Meta-Analytic Measure
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   Chapter 5 Feature Selection from Local Lift Dependence-Based Partitions
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   Chapter 6 Probabilistic Inference of Surface Heat Flux Densities from Infrared Thermography
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   Chapter 7 Schrödinger’s Zebra: Applying Mutual Information Maximization to Graphical Halftoning
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   Chapter 8 Regression of Fluctuating System Properties: Baryonic Tully–Fisher Scaling in Disk Galaxies
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    Chapter 9 Bayesian Portfolio Optimization for Electricity Generation Planning
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    Chapter 10 Bayesian Variable Selection Methods for Log-Gaussian Cox Processes
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    Chapter 11 Effect of Hindered Diffusion on the Parameter Sensitivity of Magnetic Resonance Spectra
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    Chapter 12 The Random Bernstein Polynomial Smoothing Via ABC Method
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    Chapter 13 Mean Field Studies of a Society of Interacting Agents
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    Chapter 14 The Beginnings of Axiomatic Subjective Probability
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    Chapter 15 Model Selection in the Sparsity Context for Inverse Problems in Bayesian Framework
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    Chapter 16 Sample Size Calculation Using Decision Theory
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    Chapter 17 Utility for Significance Tests
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    Chapter 18 Probabilistic Equilibrium: A Review on the Application of MAXENT to Macroeconomic Models
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    Chapter 19 Full Bayesian Approach for Signal Detection with An Application to Boat Detection on Underwater Soundscape Data
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    Chapter 20 Bayesian Support for Evolution: Detecting Phylogenetic Signal in a Subset of the Primate Family
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    Chapter 21 A Comparison of Two Methods for Obtaining a Collective Posterior Distribution
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    Chapter 22 A Nonparametric Bayesian Approach for the Two-Sample Problem
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    Chapter 23 Covariance Modeling for Multivariate Spatial Processes Based on Separable Approximations
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    Chapter 24 Uncertainty Quantification and Cumulative Distribution Function: How are they Related?
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    Chapter 25 Maximum Entropy Analysis of Flow Networks with Structural Uncertainty (Graph Ensembles)
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    Chapter 26 Optimization Employing Gaussian Process-Based Surrogates
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    Chapter 27 Bayesian and Maximum Entropy Analyses of Flow Networks with Non-Gaussian Priors and Soft Constraints
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    Chapter 28 Using the Z-Order Curve for Bayesian Model Comparison