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Linearization Techniques in Bayesian Robustness

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Robust Bayesian Analysis

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

Abstract

This paper deals with techniques which permit one to obtain the range of a posterior expectation through a sequence of linear optimizations. In the context of Bayesian robustness, the linearization algorithm plays a fundamental role. Its mathematical aspects and its connections with fractional programming procedures are reviewed and a few instances of its broad applicability are listed. At the end, some alternative approaches are briefly discussed.

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Authors
  1. Michael Lavine
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  2. Marco Perone Pacifico
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  3. Gabriella Salinetti
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  4. Luca Tardella
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Editor information

Editors and Affiliations

  1. ESCET-URJC, Tulipan s/n, 28933, Mostoles, Madrid, Spain

    David Ríos Insua

  2. CNR IAMI, Via Ampere 56, I-20131, Milano, Italy

    Fabrizio Ruggeri

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© 2000 Springer Science+Business Media New York

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Lavine, M., Pacifico, M.P., Salinetti, G., Tardella, L. (2000). Linearization Techniques in Bayesian Robustness. In: Insua, D.R., Ruggeri, F. (eds) Robust Bayesian Analysis. Lecture Notes in Statistics, vol 152. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1306-2_14

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  • DOI: https://doi.org/10.1007/978-1-4612-1306-2_14

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98866-5

  • Online ISBN: 978-1-4612-1306-2

  • eBook Packages: Springer Book Archive

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