Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis.

Abstract

As Bayesian methods become more popular among behavioral scientists, they will inevitably be applied in situations that violate the assumptions underpinning typical models used to guide statistical inference. With this in mind, it is important to know something about how robust Bayesian methods are to the violation of those assumptions. In this paper, we… (More)
DOI: 10.3758/s13428-017-0854-1

Topics

Figures and Tables

Sorry, we couldn't extract any figures or tables for this paper.