We provide an overview of robust Bayesian analysis with emphasis on foundational,
decision oriented and computational approaches. Common types of robustness analyses …

Sensitivity Analysis for Bayesian Hierarchical Models Page 1 Bayesian Analysis (2015) 10,
Number 2, pp. 321–349 Sensitivity Analysis for Bayesian Hierarchical Models Ma lgorzata …

Stock recruit functions are important in fisheries stock assessment, but there is often
uncertainty surrounding the appropriate stock recruit model and its parameter values …

This paper reviews the growing field of Bayesian prediction. Bayesian point and interval
prediction are defined and situated in statistical prediction more generally. Then, four …

In this paper we develop a general framework of Bayesian influence analysis for assessing
various perturbation schemes to the data, the prior and the sampling distribution for a class …

Whereas a global approach to prior robustness focusses on the range of inferences arising
from a range of priors, the local approach is concerned with derivatives of posterior …

Any Bayesian analysis involves combining information represented through different model
components, and when different sources of information are in conflict it is important to detect …

Bayesian Quantile Regression with Mixed Discrete and Nonignorable Missing Covariates
Page 1 Bayesian Analysis (2020) 15, Number 2, pp. 579–604 Bayesian Quantile Regression …

In settings where parametric inference is inconsistent under model misspecification, the
discrepancy between correct and misspecified inferences is compared with the discrepancy …

This article defines a quantized entropy and develops Bayes estimates and inference for the
entropy and a Kullback–Leibler information index of the model fit. We use a Dirichlet process …