Interdisciplinary bayesian statistics [electronic resource] : EBEB 2014 / edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto.
Brazilian Meeting on Bayesian Statistics (12th : 2014 : Atibaia, Brazil); Polpo, Adriano, editor.; Louzada, Francisco, editor.; Rifo, Laura L. R., editor.; Stern, Julio M., editor.; Lauretto, Marcelo de Souza, editor.
2015
QA279.5
Available Online

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Title
Interdisciplinary bayesian statistics [electronic resource] : EBEB 2014 / edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto.
Meeting Name
Brazilian Meeting on Bayesian Statistics (12th : 2014 : Atibaia, Brazil)
ISBN
9783319124544 electronic book
3319124544 electronic book
9783319124537
DOI
https://doi.org/10.1007/978-3-319-12454-4
Published
Cham : Springer, 2015.
Language
English
Description
1 online resource (xviii, 366 pages) : illustrations.
Item Number
10.1007/978-3-319-12454-4 doi
Call Number
QA279.5
Dewey Decimal Classification
519.5/42
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 4, 2015).
Added Author
Polpo, Adriano, editor.
Louzada, Francisco, editor.
Rifo, Laura L. R., editor.
Stern, Julio M., editor.
Lauretto, Marcelo de Souza, editor.
Series
Springer proceedings in mathematics & statistics ; v. 118.
Available in Other Form
Print version: 9783319124537
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Online Access
Record Appears in
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All Resources
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.

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