Bayesian Inference and Maximum Entropy Methods in Science and Engineering

1. Front Matter

   Pages i-xvi

   PDF

2. Quantum Phases in Entropic Dynamics

   • Nicholas Carrara, Ariel Caticha

   Pages 1-11

3. Bayesian Approach to Variable Splitting Forward Models

   • Ali Mohammad-Djafari, Mircea Dumitru, Camille Chapdelaine, Li Wang

   Pages 13-23

4. Prior Shift Using the Ratio Estimator

   • Afonso Vaz, Rafael Izbicki, Rafael Bassi Stern

   Pages 25-35

5. Bayesian Meta-Analytic Measure

   • Camila B. Martins, Carlos A. de B. Pereira, Adriano Polpo

   Pages 37-42

6. Feature Selection from Local Lift Dependence-Based Partitions

   • Diego Marcondes, Adilson Simonis, Junior Barrera

   Pages 43-53

7. Probabilistic Inference of Surface Heat Flux Densities from Infrared Thermography

   • D. Nille, U. von Toussaint, B. Sieglin, M. Faitsch

   Pages 55-64

8. Schrödinger’s Zebra: Applying Mutual Information Maximization to Graphical Halftoning

   • Antal Spector-Zabusky, Donald Spector

   Pages 65-75

9. Regression of Fluctuating System Properties: Baryonic Tully–Fisher Scaling in Disk Galaxies

   • Geert Verdoolaege

   Pages 77-87

10. Bayesian Portfolio Optimization for Electricity Generation Planning

    • Hellinton H. Takada, Julio M. Stern, Oswaldo L. V. Costa, Celma de O. Ribeiro

    Pages 89-99

11. Bayesian Variable Selection Methods for Log-Gaussian Cox Processes

    • Jony Arrais Pinto Junior, Patrícia Viana da Silva

    Pages 101-110

12. Effect of Hindered Diffusion on the Parameter Sensitivity of Magnetic Resonance Spectra

    • Keith A. Earle, Troy Broderick, Oleks Kazakov

    Pages 111-121

13. The Random Bernstein Polynomial Smoothing Via ABC Method

    • Leandro A. Ferreira, Victor Fossaluza

    Pages 123-129

14. Mean Field Studies of a Society of Interacting Agents

    • Lucas Silva Simões, Nestor Caticha

    Pages 131-140

15. The Beginnings of Axiomatic Subjective Probability

    • Marcio A. Diniz, Sandro Gallo

    Pages 141-154

16. Model Selection in the Sparsity Context for Inverse Problems in Bayesian Framework

    • Mircea Dumitru, Li Wang, Ali Mohammad-Djafari, Nicolas Gac

    Pages 155-165

17. Sample Size Calculation Using Decision Theory

    • Milene Vaiano Farhat, Nicholas Wagner Eugenio, Victor Fossaluza

    Pages 167-176

18. Utility for Significance Tests

    • Nathália Demetrio Vasconcelos Moura, Sergio Wechsler

    Pages 177-186

19. Probabilistic Equilibrium: A Review on the Application of MAXENT to Macroeconomic Models

    • Paulo Hubert, Julio M. Stern

    Pages 187-197

20. Full Bayesian Approach for Signal Detection with An Application to Boat Detection on Underwater Soundscape Data

    • Paulo Hubert, Julio M. Stern, Linilson Padovese

    Pages 199-209

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 inferenceto illuminate the foundations of physical theories, are also of keen interest.

• Entropy
• Imprecise Probability
• Maximum Entropy
• Biostatistics
• Non-parametric Mmethods
• Survival Analysis
• Statistical Models
• Astrophysics
• Chemistry
• Communications Theory
• Comology
• Climate Studies
• Earth Science
• Fluid Mechanics
• Genetics
• Geophysics
• Machine Learning
• Material Science
• Medical Imaging
• Robotics

• Department of Statistics, Federal University of São Carlos, São Carlos, Brazil

  Adriano Polpo, Rafael Izbicki

• Applied Mathematics, University of São Paulo, São Paulo, Brazil

  Julio Stern

• Institute of Mathematical Sciences and Computing, University of São Paulo, São Paulo, Brazil

  Francisco Louzada

• Itaú Asset Management, Banco Itaú-Unibanco, São Paulo, Brazil

  Hellinton Takada

Adriano Polpo, Ph.D., is an Associate Professor of Statistics at the Federal University of Sao Carlos (UFSCar, Brazil). He received his B.Sc. in Statistics from the State University of Campinas, his Ph.D. in Statistics from the University of Sao Paulo, and he was Research Associate at Florida State University. He is the coordinator of the research group GIS (Group of Inductive Statistics), and is the coauthor of more than 30 publications in statistical peer-reviewed journals, books, and book chapters. Dr. Polpo has served as Head of the Department of Statistics at UFSCar and President of the ISBrA (the Brazilian Chapter of the International Society for Bayesian Analysis). 

Julio Michael Stern, Ph.D., is a Full Professor at the IME-USP (Institute of Mathematics and Statistics of the University of Sao Paulo) and Research Fellow of the CNPq (Brazilian National Council for Science and Technology). He received his B.Sc. and M.Sc. in Physics from the University of Sao Paulo, his Ph.D. in Operations Research from Cornell University, and his Liv.Doc. in Computer Science from the University of Sao Paulo. He was President of the ISBrA (Brazilian Chapter of the International Society for Bayesian Analysis) from 2010 to 2012 and the organizer of MaxEnt 2008. He has published several books and articles in the areas of Epistemology and Logic, Mathematical Modeling and Operations Research, Statistical Theory and Methods, and Sparse and Structured Systems. 

Francisco Louzada, Ph.D., is a Full Professor of Statistics at the Department of Applied Mathematics and Statistics, University of Sao Paulo (ICMC-USP, Brazil); Research Productivity Fellow of the Brazilian founding agency CNPq, Level 1; Director of the Center for Risk Analysis (CER); Director of the Center for Applied Mathematics and Statistics in Industry (CeMEAI); Director of Technology Transfer, and Executive Director of External Relations at the Center for Research, Innovation and Dissemination of Mathematical Science in Industry (CEPIDCeMEAI). He received his Ph.D. in Statistics from the University of Oxford. Dr. Louzada is the author or coauthor of more than 200 publications in statistical peer-reviewed journals, books, and book chapters. He has supervised more than 100 assistant researchers, post-docs, graduate students, and undergraduates. 

Rafael Izbicki, Ph.D., is an Assistant Professor of Statistics at the Federal University of Sao Carlos (UFSCar, Brazil). He received his Ph.D. in Statistics from Carnegie Mellon University (CMU, USA) and his B.A. from the University of Sao Paulo (IME-USP, Brazil). He has published several papers in the areas of machine learning, nonparametric statistics, foundations of statistics, decision theory, Bayesian statistics, and high-dimensional inference. 

Hellinton Hatsuo Takada, Ph.D., is Vice President of Quantitative Research at Itaú Asset Management andis co-founder and director of Blitz-Trading. Additionally, he is a Professor at the Institute of Management Foundation (FIA, Brazil), the Assunção University Center-Pontifical Catholic University (UNIFAI-PUC, Brazil) and the Carlos Drummond de Andrade Group (GCDA, Brazil). He received his B.S., M.A., and Ph.D. degrees in Engineering from the Aeronautical Institute of Technology (ITA, Brazil), and he did his post-doctorate at the Institute of Mathematics and Statistics of the University of São Paulo (IME-USP, Brazil). He is certified with CPA-20 and CGA from the Brazilian Financial and Capital Markets Association (ANBIMA, Brazil) and has a Certificate in Quantitative Finance (CQF) from CQF Institute, UK. His research interests include finance, macroeconomic modeling, Bayesian statistics, information theory, and numerical optimization.

 

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