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Interdisciplinary Bayesian Statistics

EBEB 2014

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Adriano Polpo

    1. Federal University of Sao Carlos, Sao Carlos, Brazil

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  2. Francisco Louzada

    1. University of Sao Paulo, Sao Carlos, Brazil

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  3. Laura L. R. Rifo

    1. Campinas State University, Campinas, Brazil

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  4. Julio M. Stern

    1. Dept. of Applied Mathematics, University of Sao Paulo Institute of Mathematics and Statistics, Sao Paulo, Brazil

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  5. Marcelo Lauretto

    1. School of Arts, Sciences and Humanities, University of Sao Paulo, Sao Paulo, Brazil

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  • Research showcased here comes from international scholars, who presented at EBEB 2014 - XII Brazilian Meeting on Bayesian Statistics
  • Conference and refereed papers here showcase Bayesian Statistics, from theoretical questions to solving problems with real word data
  • EBEB is held by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of ISBA (the International Society for Bayesian Analysis)
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (30 papers)

  1. Front Matter

    Pages i-xviii
    Download chapter PDF

  2. What About the Posterior Distributions When the Model is Non-dominated?

    • Claudio Macci, Fabio Spizzichino
    Pages 1-11

  3. Predictive Inference Under Exchangeability, and the Imprecise Dirichlet Multinomial Model

    • Gert de Cooman, Jasper De Bock, Márcio Diniz
    Pages 13-33

  4. Bayesian Learning of Material Density Function by Multiple Sequential Inversions of 2-D Images in Electron Microscopy

    • Dalia Chakrabarty, Shashi Paul
    Pages 35-48

  5. Problems with Constructing Tests to Accept the Null Hypothesis

    • André Rogatko, Steven Piantadosi
    Pages 49-54

  6. Cognitive-Constructivism, Quine, Dogmas of Empiricism, and Münchhausen’s Trilemma

    • Julio Michael Stern
    Pages 55-68

  7. A Maximum Entropy Approach to Learn Bayesian Networks from Incomplete Data

    • Giorgio Corani, Cassio P. de Campos
    Pages 69-82

  8. Bayesian Inference in Cumulative Distribution Fields

    • Ricardo Silva
    Pages 83-95

  9. MCMC-Driven Adaptive Multiple Importance Sampling

    • Luca Martino, Víctor Elvira, David Luengo, Jukka Corander
    Pages 97-109

  10. Bayes Factors for Comparison of Restricted Simple Linear Regression Coefficients

    • Viviana Giampaoli, Carlos A. B. Pereira, Heleno Bolfarine, Julio M. Singer
    Pages 111-123

  11. A Spanning Tree Hierarchical Model for Land Cover Classification

    • Hunter Glanz, Luis Carvalho
    Pages 125-134

  12. Nonparametric Bayesian Regression Under Combinations of Local Shape Constraints

    • Khader Khadraoui
    Pages 135-147

  13. A Bayesian Approach to Predicting Football Match Outcomes Considering Time Effect Weight

    • Francisco Louzada, Adriano K. Suzuki, Luis E. B. Salasar, Anderson Ara, José G. Leite
    Pages 149-162

  14. Homogeneity Tests for 2×2 Contingency Tables

    • Natalia Oliveira, Marcio Diniz, Adriano Polpo
    Pages 163-171

  15. Combining Optimization and Randomization Approaches for the Design of Clinical Trials

    • Victor Fossaluza, Marcelo de Souza Lauretto, Carlos Alberto de Bragança Pereira, Julio Michael Stern
    Pages 173-184

  16. Factor Analysis with Mixture Modeling to Evaluate Coherent Patterns in Microarray Data

    • Joao Daniel Nunes Duarte, Vinicius Diniz Mayrink
    Pages 185-195

  17. Bayesian Hypothesis Testing in Finite Populations: Bernoulli Multivariate Variables

    • Brian Alvarez R. de Melo, Luis Gustavo Esteves
    Pages 197-205

  18. Bayesian Ridge-Regularized Covariance Selection with Community Behavior in Latent Gaussian Graphical Models

    • Lijun Peng, Luis E. Carvalho
    Pages 207-216

  19. Bayesian Inference of Deterministic Population Growth Models

    • Luiz Max Carvalho, Claudio J. Struchiner, Leonardo S. Bastos
    Pages 217-228

  20. A Weibull Mixture Model for the Votes of a Brazilian Political Party

    • Rosineide F. da Paz, Ricardo S. Ehlers, Jorge L. Bazán
    Pages 229-241
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Keywords

About this book

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.

Editors and Affiliations

  • Federal University of Sao Carlos, Sao Carlos, Brazil

    Adriano Polpo

  • University of Sao Paulo, Sao Carlos, Brazil

    Francisco Louzada

  • Campinas State University, Campinas, Brazil

    Laura L. R. Rifo

  • Dept. of Applied Mathematics, University of Sao Paulo Institute of Mathematics and Statistics, Sao Paulo, Brazil

    Julio M. Stern

  • School of Arts, Sciences and Humanities, University of Sao Paulo, Sao Paulo, Brazil

    Marcelo Lauretto

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