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Case Studies in Bayesian Statistics

Volume III

  • Constantine Gatsonis
  • James S. Hodges
  • Robert E. Kass
  • Robert McCulloch
  • Peter Rossi
  • Nozer D. Singpurwalla

Part of the Lecture Notes in Statistics book series (LNS, volume 121)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Invited Papers

  3. Contributed Papers

    1. Front Matter
      Pages 303-303
    2. Bruce A. Craig, Michael A. Newton
      Pages 305-323
    3. Alaattin Erkanli, Refik Soyer, Dalene Stangl
      Pages 325-346
    4. Katja Ickstadt, Robert L. Wolpert
      Pages 371-385
    5. Agostino Nobile, Chandra R. Bhat, Eric I. Pas
      Pages 419-434
    6. Elizabeth H. Slate, Kathleen A. Cronin
      Pages 435-456
    7. L. J. Wolfson, J. B. Kadane, M. J. Small
      Pages 457-468
  4. Back Matter
    Pages 469-474

About these proceedings

Introduction

Like the first two volumes, this third volume of case studies presents detailed applications of Bayesian statistical analysis, emphasizing the sci­ entific context. The papers were presented and discussed at a workshop at Carnegie Mellon University, October 5-7, 1995. In this volume, which is dedicated to the memory of Morris H. DeGroot, econometric applica­ tions are highlighted. There are six invited papers, each with accompany­ ing invited discussion, and eight contributed papers (which were selected following refereeing). In addition, we include prefatory recollections about Morrie DeGroot by James o. Berger and Richard M. Cyert. INVITED PAPERS In Probing Public Opinion: The State of Valencia Experience, Jose Bernardo, who was a scientific advisor to the President of the State of Valencia, Spain, summarizes procedures that were set up to probe public opinion, and were used as an input to the government's decision making process. At the outset, a sample survey had to be designed. The problem of finding an optimal Bayesian design, based on logarithmic divergence be­ tween probability distributions, involves minimization over 21483 points in the action space. To solve it, simulated annealing was used. The author describes the objective of obtaining the probability that an individual clas­ sified in a certain group will prefer one of several possible alternatives, and his approach using posterior distributions based on reference priors.

Keywords

Bayesian inference Project Statistica bayesian statistics computer history of mathematics homogenization marketing model modeling selection statistics story university variable

Editors and affiliations

  • Constantine Gatsonis
    • 1
  • James S. Hodges
    • 2
  • Robert E. Kass
    • 3
  • Robert McCulloch
    • 4
  • Peter Rossi
    • 4
  • Nozer D. Singpurwalla
    • 5
  1. 1.Center for Statistical SciencesBrown UniversityProvidenceUSA
  2. 2.Division of BiostatisticsUniversity of Minnesota-Twin Cities, School of Public HealthMinneapolisUSA
  3. 3.Department of StatisticsCarnegie-Mellon UniversityPittsburghUSA
  4. 4.Graduate School of BusinessUniversity of ChicagoChicagoUSA
  5. 5.Department of Operations ResearchThe George Washington UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-2290-3
  • Copyright Information Springer-Verlag New York, Inc. 1997
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94990-1
  • Online ISBN 978-1-4612-2290-3
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site
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