Open Access
June 2015 Sensitivity Analysis for Bayesian Hierarchical Models
Małgorzata Roos, Thiago G. Martins, Leonhard Held, Håvard Rue
Bayesian Anal. 10(2): 321-349 (June 2015). DOI: 10.1214/14-BA909


Prior sensitivity examination plays an important role in applied Bayesian analyses. This is especially true for Bayesian hierarchical models, where interpretability of the parameters within deeper layers in the hierarchy becomes challenging. In addition, lack of information together with identifiability issues may imply that the prior distributions for such models have an undesired influence on the posterior inference. Despite its importance, informal approaches to prior sensitivity analysis are currently used. They require repetitive re-fits of the model with ad-hoc modified base prior parameter values. Other formal approaches to prior sensitivity analysis suffer from a lack of popularity in practice, mainly due to their high computational cost and absence of software implementation. We propose a novel formal approach to prior sensitivity analysis, which is fast and accurate. It quantifies sensitivity without the need for a model re-fit. Through a series of examples we show how our approach can be used to detect high prior sensitivities of some parameters as well as identifiability issues in possibly over-parametrized Bayesian hierarchical models.


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Małgorzata Roos. Thiago G. Martins. Leonhard Held. Håvard Rue. "Sensitivity Analysis for Bayesian Hierarchical Models." Bayesian Anal. 10 (2) 321 - 349, June 2015.


Published: June 2015
First available in Project Euclid: 2 February 2015

zbMATH: 1335.62059
MathSciNet: MR3420885
Digital Object Identifier: 10.1214/14-BA909

Keywords: Base prior , Bayesian hierarchical models , Bayesian robustness , Calibration , formal local sensitivity measure , Hellinger distance , Identifiability , overparametrisation

Rights: Copyright © 2015 International Society for Bayesian Analysis

Vol.10 • No. 2 • June 2015
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