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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
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
  17. Rafael Catoia Pulgrossi, Natalia Lombardi Oliveira, Adriano Polpo, Rafael Izbicki
    Pages 221-230
  18. Rafael de C. Ceregatti, Rafael Izbicki, Luis Ernesto B. Salasar
    Pages 231-241
  19. Rafael S. Erbisti, Thais C. O. Fonseca, Mariane B. Alves
    Pages 243-252
  20. Robert K. Niven, Michael Schlegel, Markus Abel, Steven H. Waldrip, Roger Guimera
    Pages 261-274
  21. R. Preuss, U. von Toussaint
    Pages 275-284
  22. R. Wesley Henderson, Paul M. Goggans
    Pages 295-304

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

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-91143-4
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-91142-7
  • Online ISBN 978-3-319-91143-4
  • Series Print ISSN 2194-1009
  • Series Online ISSN 2194-1017
  • Buy this book on publisher's site