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Bayesian Inference and Maximum Entropy Methods in Science and Engineering: MaxEnt 37, Jarinu, Brazil, July 09-14, 2017

Bayesian Inference and Maximum Entropy Methods in Science and Engineering: MaxEnt 37, Jarinu, Brazil, July 09-14, 2017
Catalogue Information
Field name Details
Dewey Class 519 (DDC 21.)
Title Bayesian Inference and Maximum Entropy Methods in Science and Engineering (M) : MaxEnt 37, Jarinu, Brazil, July 09-14, 2017 / Adriano Polpo, Julio Stern, Francisco Louzada, Rafael Izbicki, Hellinton Takada, editors.
Author International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (37th : 2017 : Jarinu, Brazil)
Added Personal Name Polpo, Adriano
Louzada, Francisco
Stern, Julio
Izbicki, Rafael
Takada, Hellinton
Publication Cham, Switzerland : Springer , [2018]
Physical Details xvi, 304 pages : ill. ; 25 cm
Series Springer Proceedings in Mathematics & Statistics ; 29
ISBN 9783319911427
Summary Note "These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in São Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community. They gather research from scholars in many different fields who use inductive statistics methods and focus on the foundations of the Bayesian paradigm, their comparison to objectivistic or frequentist statistics counterparts, and their appropriate applications. Interest in the foundations of inductive statistics has been growing with the increasing availability of Bayesian methodological alternatives, and scientists now face much more difficult choices in finding the optimal methods to apply to their problems. By carefully examining and discussing the relevant foundations, the scientific community can avoid applying Bayesian methods on a merely ad hoc basis. For over 35 years, the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering application contexts. The workshops welcome contributions on all aspects of probabilistic inference, including novel techniques and applications, and work that sheds new light on the foundations of inference. Areas of application in these workshops include astronomy and astrophysics, chemistry, communications theory, cosmology, climate studies, earth science, fluid mechanics, genetics, geophysics, machine learning, materials science, medical imaging, nanoscience, source separation, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics, and the social sciences. Bayesian computational techniques such as Markov chain Monte Carlo sampling are also regular topics, as are approximate inferential methods. Foundational issues involving probability theory and information theory, as well as novel applications of inference to illuminate the foundations of physical theories, are also of keen interest.":
Contents note 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 DiscussionReferences; 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 Introduction2 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; 6 Conclusion; References Regression of Fluctuating System Properties: Baryonic Tully-Fisher Scaling in Disk Galaxies1 Introduction; 2 GLS Regression: Principles and Motivation; 3 Application of GLS to Tully-Fisher Scaling; 3.1 The Baryonic Tully-Fisher Relation; 3.2 Regression Analysis; 4 Conclusion; References; Bayesian Portfolio Optimization for Electricity Generation Planning; 1 Introduction; 2 Classical Approach; 3 Bayesian Approach; 3.1 Improper Prior Case; 3.2 Proper Prior Case; 4 Results; 5 Final Remarks; References; Bayesian Variable Selection Methods for Log-Gaussian Cox Processes; 1 Introduction 2 Spatial Point Pattern Process2.1 Log-Gaussian Cox Process; 3 Bayesian Variable Selection; 3.1 Kuo and Mallick (KM); 3.2 Gibbs Variable Selection (GVS); 3.3 Stochastic Search Variable Selection (SSVS); 3.4 Comparing the Methods; 4 Simulation Study; 4.1 Prior Distributions; 4.2 Landscapes Definitions; 4.3 Results; 5 Conclusion; References; Effect of Hindered Diffusion on the Parameter Sensitivity of Magnetic Resonance Spectra; 1 Introduction; 2 Magnetic Resonance; 3 Hindered Diffusion; 3.1 Recurrence; 3.2 Coordinate Systems; 4 Simple Model; 5 Discussion; References
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0000000044277 519.24 BAY
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