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© 2018

Bayesian Inference and Maximum Entropy Methods in Science and Engineering

MaxEnt 37, Jarinu, Brazil, July 09–14, 2017

  • Adriano Polpo
  • Julio Stern
  • Francisco Louzada
  • Rafael Izbicki
  • Hellinton Takada

Benefits

  • Presents cutting-edge research from a wide variety of science and engineering fields that use inductive statistics

  • Examines and discusses the foundations of inductive statistics, addressing the growing difficulty in choosing the optimal method to apply to problems due to the increasing availability of Bayesian methodological alternatives

  • Expands the available research on Bayesian methods and promotes their application in the scientific community

Conference proceedings maxent 2017

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 239)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Nicholas Carrara, Ariel Caticha
    Pages 1-11
  3. Ali Mohammad-Djafari, Mircea Dumitru, Camille Chapdelaine, Li Wang
    Pages 13-23
  4. Afonso Vaz, Rafael Izbicki, Rafael Bassi Stern
    Pages 25-35
  5. Camila B. Martins, Carlos A. de B. Pereira, Adriano Polpo
    Pages 37-42
  6. Diego Marcondes, Adilson Simonis, Junior Barrera
    Pages 43-53
  7. D. Nille, U. von Toussaint, B. Sieglin, M. Faitsch
    Pages 55-64
  8. Hellinton H. Takada, Julio M. Stern, Oswaldo L. V. Costa, Celma de O. Ribeiro
    Pages 89-99
  9. Jony Arrais Pinto Junior, Patrícia Viana da Silva
    Pages 101-110
  10. Keith A. Earle, Troy Broderick, Oleks Kazakov
    Pages 111-121
  11. Leandro A. Ferreira, Victor Fossaluza
    Pages 123-129
  12. Lucas Silva Simões, Nestor Caticha
    Pages 131-140
  13. Marcio A. Diniz, Sandro Gallo
    Pages 141-154
  14. Mircea Dumitru, Li Wang, Ali Mohammad-Djafari, Nicolas Gac
    Pages 155-165
  15. Milene Vaiano Farhat, Nicholas Wagner Eugenio, Victor Fossaluza
    Pages 167-176
  16. Nathália Demetrio Vasconcelos Moura, Sergio Wechsler
    Pages 177-186

About these proceedings

Introduction

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.

Keywords

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

Editors and affiliations

  • Adriano Polpo
    • 1
  • Julio Stern
    • 2
  • Francisco Louzada
    • 3
  • Rafael Izbicki
    • 4
  • Hellinton Takada
    • 5
  1. 1.Department of StatisticsFederal University of São CarlosSão CarlosBrazil
  2. 2.Applied MathematicsUniversity of São PauloSão PauloBrazil
  3. 3.Institute of Mathematical Sciences and ComputingUniversity of São PauloSão PauloBrazil
  4. 4.Department of StatisticsFederal University of São CarlosSão CarlosBrazil
  5. 5.Itaú Asset ManagementBanco Itaú-UnibancoSão PauloBrazil

About the editors

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 and is 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.

 

Bibliographic information