Titlebook: Robust Bayesian Analysis; David Ríos Insua,Fabrizio Ruggeri Book 2000 Springer Science+Business Media New York 2000 Markov chain Monte Car

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書目名稱Robust Bayesian Analysis影響因子(影響力)




書目名稱Robust Bayesian Analysis影響因子(影響力)學(xué)科排名




書目名稱Robust Bayesian Analysis網(wǎng)絡(luò)公開度




書目名稱Robust Bayesian Analysis網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Robust Bayesian Analysis被引頻次




書目名稱Robust Bayesian Analysis被引頻次學(xué)科排名




書目名稱Robust Bayesian Analysis年度引用




書目名稱Robust Bayesian Analysis年度引用學(xué)科排名




書目名稱Robust Bayesian Analysis讀者反饋




書目名稱Robust Bayesian Analysis讀者反饋學(xué)科排名

Spartan

https://doi.org/10.1007/978-1-4612-1306-2Markov chain Monte Carlo; Scheme; Volume; computation; decision problem; likelihood; maintenance; metrics; m

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On the Use of the Concentration Function in Bayesian RobustnessWe present applications of the concentration function in both global and local sensitivity analyses, along with its connection with Choquet capacities.

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漂浮

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Bayesian Robustnesse need for a robust approach. Common types of robustness analyses are illustrated, including global and local sensitivity analysis and loss and likelihood robustness. Relationships with other approaches are also discussed. Finally, possible directions for future research are outlined.

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Topics on the Foundations of Robust Bayesian Analysisich underpins Bayesian inference and decision analysis. This chapter surveys whether a similar situation holds for robust Bayesian analysis, overviewing foundational results leading to standard computations in robust Bayesian analysis.

領(lǐng)袖氣質(zhì)

Stability of Bayes Decisions and Applications Chuang, who gave the initial formulation of the problem, the article focuses on a major contribution due to Salinetti and the subsequent developments. The discussion also includes applications of stability to the local and global robustness issues relating to the prior distribution and the loss function of a Bayes decision problem.

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Local Robustness in Bayesian Analysisries of local sensitivity often have nonsensical asymptotic behaviour, raising thorny questions about their calibration. In this article the local approach is broadly defined, ranging from differentiation of functions (e.g., assessing sensitivity to hyperparameters) to differentiation of functionals