Interdisciplinary Bayesian Statistics (eBook, PDF)

EBEB 2014
Redaktion: Polpo, Adriano; Lauretto, Marcelo; Stern, Julio M.; Rifo, Laura L. R.; Louzada, Francisco

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 366
• Erscheinungstermin: 25. Februar 2015
• Englisch
• ISBN-13: 9783319124544
• Artikelnr.: 43800231

Inhaltsangabe

What About the Posterior Distributions When the Model is Non-dominated.- Bayesian Learning of Material Density Function by Multiple Sequential Inversions of 2-D Images in Electron Microscopy.- Problems with Constructing Tests to Accept the Null Hypothesis.- Cognitive-Constructivism, Quine, Dogmas of Empiricism, and Munchhausen's Trilemma.- A maximum entropy approach to learn Bayesian networks from incomplete data.- Bayesian Inference in Cumulative Distribution Fields.- MCMC-Driven Adaptive Multiple Importance Sampling.- Bayes Factors for comparison of restricted simple linear regression coefficients.- A Spanning Tree Hierarchical Model for Land Cover Classification.- Nonparametric Bayesian regression under combinations of local shape constraints.- A Bayesian Approach to Predicting Football Match Outcomes Considering Time Effect Weight.- Homogeneity tests for 22 contingency tables.- Combining Optimization and Randomization Approaches for the Design of Clinical Trials.- Factor analysis with mixture modeling to evaluate coherent patterns in microarray data.