Robust Bayesian Analysis

  • David Ríos Insua
  • Fabrizio Ruggeri

Part of the Lecture Notes in Statistics book series (LNS, volume 152)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. James O. Berger, David Ríos Insua, Fabrizio Ruggeri
      Pages 1-32
  3. Foundations

    1. Front Matter
      Pages 33-33
    2. David Ríos Insua, Regino Criado
      Pages 33-44
  4. Global and Local Robustness

    1. Front Matter
      Pages 45-45
    2. Paul Gustafson
      Pages 71-88
    3. Sandra Fortini, Fabrizio Ruggeri
      Pages 109-126
  5. Likelihood Robustness

    1. Front Matter
      Pages 127-127
    2. N. D. Shyamalkumar
      Pages 127-143
  6. Loss Robustness

    1. Front Matter
      Pages 145-145
    2. Dipak K. Dey, Athanasios C. Micheas
      Pages 145-159
    3. Jacinto Martín, J. Pablo Arias
      Pages 161-186
    4. Joseph Kadane, Gabriella Salinetti, Cidambi Srinivasan
      Pages 187-196
  7. Comparison With Other Statistical Methods

    1. Front Matter
      Pages 197-197
    2. Brunero Liseo
      Pages 197-222
  8. Algorithms

    1. Front Matter
      Pages 261-261
    2. Michael Lavine, Marco Perone Pacifico, Gabriella Salinetti, Luca Tardella
      Pages 261-272
    3. Bruno Betrò, Alessandra Guglielmi
      Pages 273-293
  9. Case Studies

    1. Front Matter
      Pages 317-317
    2. Concha Bielza, Sixto Ríos-Insua, Manuel Gómez, Juan A. Fernández del Pozo
      Pages 317-334
    3. Enrico Cagno, Franco Caron, Mauro Mancini, Fabrizio Ruggeri
      Pages 335-350
    4. Bradley P. Carlin, María-Eglée Pérez
      Pages 351-372
    5. Simon P. Wilson, Michael P. Wiper
      Pages 385-400
  10. Back Matter
    Pages 401-424

About this book


Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness holds and no further analysis and refinement would be required. Robust Bayesian analysis has been widely accepted by Bayesian statisticians; for a while it was even a main research topic in the field. However, to a great extent, their impact is yet to be seen in applied settings. This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in­ terest in the area. The papers in the volume are divided into nine parts covering the main aspects of the field. The first one provides an overview of Bayesian robustness at a non-technical level. The paper in Part II con­ cerns foundational aspects and describes decision-theoretical axiomatisa­ tions leading to the robust Bayesian paradigm, motivating reasons for which robust analysis is practically unavoidable within Bayesian analysis.


Markov chain Monte Carlo Scheme Volume computation decision problem likelihood maintenance metrics minimum model reliability selection software stability techniques

Editors and affiliations

  • David Ríos Insua
    • 1
  • Fabrizio Ruggeri
    • 2
  1. 1.ESCET-URJCMostoles, MadridSpain
  2. 2.CNR IAMIMilanoItaly

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 2000
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98866-5
  • Online ISBN 978-1-4612-1306-2
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site