Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Info and Reading Options

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Intro -- Preface -- Contents -- Contributors -- Quantum Phases in Entropic Dynamics -- 1 Introduction -- 2 Entropic Dynamics - A Brief Review -- 3 Gauge Symmetry and Multi-Valued Phases -- 4 Discussion -- References -- Bayesian Approach to Variable Splitting Forward Models -- 1 Introduction -- 2 Forward Model 1 -- 3 Forward Model 2 -- 4 Forward Model 3 -- 5 Forward Models 4 and 5 -- 6 Forward Models 6 and 7 -- 7 Conclusions -- References -- Prior Shift Using the Ratio Estimator -- 1 Introduction -- 2 Setting and Goals -- 3 Quantification Methods -- 3.1 The Classify and Count Estimator (CCE) -- 3.2 The Ratio Estimator (RE) -- 4 Experiments -- 5 Final Discussion -- References -- Bayesian Meta-Analytic Measure -- 1 Introduction -- 2 Meta-Analysis Measure -- 3 Example -- 4 Final Remarks -- References -- Feature Selection from Local Lift Dependence-Based Partitions -- 1 Introduction -- 2 Local Lift Dependence -- 3 Feature Selection Algorithm from Local Lift Dependence-Based Partitions -- 3.1 Classical Feature Selection Algorithm -- 3.2 Local Lift Dependence-Based Partitions -- 3.3 Cost Functions -- 3.4 Stopping Criteria for the Algorithm -- 4 Applications -- 5 Final Remarks -- References -- Probabilistic Inference of Surface Heat Flux Densities from Infrared Thermography -- 1 Introduction -- 2 The Measurement System -- 3 Forward Model -- 3.1 Heat Diffusion -- 3.2 Measurement System -- 4 Heatflux Model: Adaptive Kernel -- 4.1 Effective Number of Degrees of Freedom (eDOF) -- 5 Exploring the Parameter Space -- 6 Synthetic Data as Benchmark -- 7 Processing Measured Data -- 8 Conclusions -- References -- Schrödinger's Zebra: Applying Mutual Information Maximization to Graphical Halftoning -- 1 Introduction -- 2 Information Theory and Halftoning -- 3 Quantum Halftoning -- 4 Implementation and Examples -- 5 Obtaining Insights Regarding Human Vision

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