Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain
inputs. This paper seeks to provide an overview of the subject, one that is accessible to …

Despite increasing interest in Bayesian approaches, especially across the social sciences, it
has been virtually impossible to find a text that introduces Bayesian data analysis in a …

This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory,
methods, and applications. Starting from basic statistics, undergraduate calculus and linear …

At last—a social scientist's guide through the pitfalls of modern statistical computing
Addressing the current deficiency in the literature on statistical methods as they apply to the …

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

An overview of the robust Bayesian approach is presented, primarily focusing on
developments in the last decade. Examples are presented to motivate the need for a robust …

Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an
important part of the Bayesian workflow. We introduce a practical and computationally …

A local approach is used to study the sensitivity of posterior expectations to distributional
assumptions. The main focus is on perturbations to the prior distribution. Two types of …

We investigate diagnostics for quantifying the effect of small changes to the prior distribution
over a k-dimensional parameter space. We show that several previously suggested …

In order to deal with mild deviations from the assumed parametric model, we propose a
procedure for accounting for model uncertainty in the Bayesian framework. In particular, in …