Robust Bayesian Analysis
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...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2000, 2000
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| Edition: | 1st ed. 2000 |
| Series: | Lecture Notes in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 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