Del 152 i serien Lecture Notes in Statistics

Robust Bayesian Analysis

Häftad, Engelska, 2000

1 399 kr

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This edited volume will present an overview of robust Bayesian methods and their applications. Chapters will explore both foundational and computational aspects together with applications. Chapters concerning foundations will describe decision theoretical axiomatisations leading to the robust Bayesian paradigm and motivate reasons for which robust analyses are practically unavoidable within Bayesian analysis.

Produktinformation

Utgivningsdatum2000-09-14
Mått155 x 235 x 24 mm
Vikt663 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Statistics
Antal sidor422
FörlagSpringer-Verlag New York Inc.
ISBN9780387988665

Tillhör följande kategorier

Matematisk statistik inom Naturvetenskap och teknik

1 Bayesian Robustness.- 2 Topics on the Foundations of Robust Bayesian Analysis.- 3 Global Bayesian Robustness for Some Classes of Prior Distributions.- 4 Local Robustness in Bayesian Analysis.- 5 Global and Local Robustness Approaches: Uses and Limitations.- 6 On the Use of the Concentration Function in Bayesian Robustness.- 7 Likelihood Robustness.- 8 Ranges of Posterior Expected Losses and ?—Robust Actions.- 9 Computing Efficient Sets in Bayesian Decision Problems.- 10 Stability of Bayes Decisions and Applications.- 11 Robustness Issues in Bayesian Model Selection.- 12 Bayesian Robustness and Bayesian Nonparametrics.- 13 ?-Minimax: A Paradigm for Conservative Robust Bayesians.- 14 Linearization Techniques in Bayesian Robustness.- 15 Methods for Global Prior Robustness under Generalized Moment Conditions.- 16 Efficient MCMC Schemes for Robust Model Extensions Using Encompassing Dirichlet Process Mixture Models.- 17 Sensitivity Analysis in IctNeo.- 18 Sensitivity of Replacement Priorities for Gas Pipeline Maintenance.- 19 Robust Bayesian Analysis in Medical and Epidemiological Settings.- 20 A Robust Version of the Dynamic Linear Model with an Economic Application.- 21 Prior Robustness in Some Common Types of Software Reliability Model.