Interdisciplinary Bayesian Statistics

EBEB 2014

  • Adriano Polpo
  • Francisco Louzada
  • Laura L. R. Rifo
  • Julio M. Stern
  • Marcelo Lauretto

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 118)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Gert de Cooman, Jasper De Bock, Márcio Diniz
    Pages 13-33
  3. André Rogatko, Steven Piantadosi
    Pages 49-54
  4. Giorgio Corani, Cassio P. de Campos
    Pages 69-82
  5. Luca Martino, Víctor Elvira, David Luengo, Jukka Corander
    Pages 97-109
  6. Viviana Giampaoli, Carlos A. B. Pereira, Heleno Bolfarine, Julio M. Singer
    Pages 111-123
  7. Hunter Glanz, Luis Carvalho
    Pages 125-134
  8. Francisco Louzada, Adriano K. Suzuki, Luis E. B. Salasar, Anderson Ara, José G. Leite
    Pages 149-162
  9. Natalia Oliveira, Marcio Diniz, Adriano Polpo
    Pages 163-171
  10. Victor Fossaluza, Marcelo de Souza Lauretto, Carlos Alberto de Bragança Pereira, Julio Michael Stern
    Pages 173-184
  11. Joao Daniel Nunes Duarte, Vinicius Diniz Mayrink
    Pages 185-195
  12. Brian Alvarez R. de Melo, Luis Gustavo Esteves
    Pages 197-205
  13. Luiz Max Carvalho, Claudio J. Struchiner, Leonardo S. Bastos
    Pages 217-228
  14. Rosineide F. da Paz, Ricardo S. Ehlers, Jorge L. Bazán
    Pages 229-241

About these proceedings

Introduction

Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesian methods by the scientific community. Individual papers range in focus from posterior distributions for non-dominated models, to combining optimization and randomization approaches for the design of clinical trials, and classification of archaeological fragments with Bayesian networks.

Keywords

Bayes' Theorem Bayesian Statistics Biostatistics & Clinical Trials Brazilian chapter ISBA Imprecise Probability Microarray Data Statistical Methodology Statistical Models in Social Sciences

Editors and affiliations

  • Adriano Polpo
    • 1
  • Francisco Louzada
    • 2
  • Laura L. R. Rifo
    • 3
  • Julio M. Stern
    • 4
  • Marcelo Lauretto
    • 5
  1. 1.Federal University of Sao CarlosSao CarlosBrazil
  2. 2.University of Sao PauloSao CarlosBrazil
  3. 3.Campinas State UniversityCampinasBrazil
  4. 4.Dept. of Applied MathematicsUniversity of Sao Paulo Institute of Mathematics and StatisticsSao PauloBrazil
  5. 5.School of Arts, Sciences and HumanitiesUniversity of Sao PauloSao PauloBrazil

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-12454-4
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-12453-7
  • Online ISBN 978-3-319-12454-4
  • Series Print ISSN 2194-1009
  • Series Online ISSN 2194-1017
  • About this book
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