LEADER 05096nam 22006735 450 001 9910299778903321 005 20200704082430.0 010 $a3-319-12454-4 024 7 $a10.1007/978-3-319-12454-4 035 $a(CKB)3710000000360276 035 $a(EBL)1998169 035 $a(OCoLC)904131855 035 $a(SSID)ssj0001452211 035 $a(PQKBManifestationID)11801498 035 $a(PQKBTitleCode)TC0001452211 035 $a(PQKBWorkID)11479978 035 $a(PQKB)10323647 035 $a(DE-He213)978-3-319-12454-4 035 $a(MiAaPQ)EBC1998169 035 $a(PPN)184496268 035 $a(EXLCZ)993710000000360276 100 $a20150225d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInterdisciplinary Bayesian Statistics $eEBEB 2014 /$fedited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (370 p.) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v118 300 $aDescription based upon print version of record. 311 $a3-319-12453-6 320 $aIncludes bibliographical references at the end of each chapters. 327 $aWhat 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. 330 $aThrough refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesian methods by the scientific community. Individual papers range in focus from posterior distributions for non-dominated models, to combining optimization and randomization approaches for the design of clinical trials, and classification of archaeological fragments with Bayesian networks. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v118 606 $aStatistics  606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 615 0$aStatistics . 615 14$aStatistical Theory and Methods. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistics, general. 676 $a519.542 702 $aPolpo$b Adriano$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLouzada$b Francisco$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRifo$b Laura L. R$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aStern$b Julio M$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLauretto$b Marcelo$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299778903321 996 $aInterdisciplinary bayesian statistics$91522522 997 $aUNINA