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The Resource Interdisciplinary Bayesian Statistics : EBEB 2014, edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto, (electronic resource)
Interdisciplinary Bayesian Statistics : EBEB 2014, edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto, (electronic resource)
Resource Information
The item Interdisciplinary Bayesian Statistics : EBEB 2014, edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Interdisciplinary Bayesian Statistics : EBEB 2014, edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 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 1014, 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 nondominated models, to combining optimization and randomization approaches for the design of clinical trials, and classification of archaeological fragments with Bayesian networks
 Language
 eng
 Extent
 XVIII, 366 p. 67 illus., 45 illus. in color.
 Contents

 What About the Posterior Distributions When the Model is Nondominated
 Bayesian Learning of Material Density Function by Multiple Sequential Inversions of 2D Images in Electron Microscopy
 Problems with Constructing Tests to Accept the Null Hypothesis
 CognitiveConstructivism, Quine, Dogmas of Empiricism, and Munchhausenâ€™s Trilemma
 A maximum entropy approach to learn Bayesian networks from incomplete data
 Bayesian Inference in Cumulative Distribution Fields
 MCMCDriven 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
 Isbn
 9783319124544
 Label
 Interdisciplinary Bayesian Statistics : EBEB 2014
 Title
 Interdisciplinary Bayesian Statistics
 Title remainder
 EBEB 2014
 Statement of responsibility
 edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto
 Language
 eng
 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 1014, 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 nondominated models, to combining optimization and randomization approaches for the design of clinical trials, and classification of archaeological fragments with Bayesian networks
 Image bit depth
 0
 LC call number
 QA276280
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName

 Polpo, Adriano.
 Louzada, Francisco.
 Rifo, Laura L. R.
 Stern, Julio M.
 Lauretto, Marcelo.
 SpringerLink
 Series statement
 Springer Proceedings in Mathematics & Statistics,
 Series volume
 118
 http://library.link/vocab/subjectName

 Statistics
 Mathematical statistics
 Statistics
 Statistical Theory and Methods
 Statistics for Life Sciences, Medicine, Health Sciences
 Statistics, general
 Label
 Interdisciplinary Bayesian Statistics : EBEB 2014, edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 What About the Posterior Distributions When the Model is Nondominated  Bayesian Learning of Material Density Function by Multiple Sequential Inversions of 2D Images in Electron Microscopy  Problems with Constructing Tests to Accept the Null Hypothesis  CognitiveConstructivism, Quine, Dogmas of Empiricism, and Munchhausenâ€™s Trilemma  A maximum entropy approach to learn Bayesian networks from incomplete data  Bayesian Inference in Cumulative Distribution Fields  MCMCDriven 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
 Dimensions
 unknown
 Extent
 XVIII, 366 p. 67 illus., 45 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783319124544
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783319124544
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783319124544
 Label
 Interdisciplinary Bayesian Statistics : EBEB 2014, edited by Adriano Polpo, Francisco Louzada, Laura L. R. Rifo, Julio M. Stern, Marcelo Lauretto, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 What About the Posterior Distributions When the Model is Nondominated  Bayesian Learning of Material Density Function by Multiple Sequential Inversions of 2D Images in Electron Microscopy  Problems with Constructing Tests to Accept the Null Hypothesis  CognitiveConstructivism, Quine, Dogmas of Empiricism, and Munchhausenâ€™s Trilemma  A maximum entropy approach to learn Bayesian networks from incomplete data  Bayesian Inference in Cumulative Distribution Fields  MCMCDriven 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
 Dimensions
 unknown
 Extent
 XVIII, 366 p. 67 illus., 45 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783319124544
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783319124544
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783319124544
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