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Interdisciplinary Bayesian Statistics: EBEB 2014 Softcover reprint of the original 1st ed. 2015 [Pehme köide]

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  • Formaat: Paperback / softback, 366 pages, kõrgus x laius: 235x155 mm, kaal: 5796 g, 45 Illustrations, color; 22 Illustrations, black and white; XVIII, 366 p. 67 illus., 45 illus. in color., 1 Paperback / softback
  • Sari: Springer Proceedings in Mathematics & Statistics 118
  • Ilmumisaeg: 05-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319352687
  • ISBN-13: 9783319352688
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  • Formaat: Paperback / softback, 366 pages, kõrgus x laius: 235x155 mm, kaal: 5796 g, 45 Illustrations, color; 22 Illustrations, black and white; XVIII, 366 p. 67 illus., 45 illus. in color., 1 Paperback / softback
  • Sari: Springer Proceedings in Mathematics & Statistics 118
  • Ilmumisaeg: 05-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319352687
  • ISBN-13: 9783319352688
Teised raamatud teemal:

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.

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 Munchhausens 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.