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Linearization Techniques in Bayesian Robustness

  • Michael Lavine
  • Marco Perone Pacifico
  • Gabriella Salinetti
  • Luca Tardella
Part of the Lecture Notes in Statistics book series (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.

Key words

fractional programming prior distributions sensitivity 

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Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Michael Lavine
  • Marco Perone Pacifico
  • Gabriella Salinetti
  • Luca Tardella

There are no affiliations available

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