SEDA: Sensitivity diagnostics for Bayesian hierarchical models

Bayesian hierarchical models (BHM) are nowadays a well established statistical methodology used for decision making. BHMs have a unique ability to incorporate external prior information in the analysis. They can be conveniently estimated by Bayesian general-purpose software systems such as Stan, JAGS and R-INLA. The most intriguing aspect of BHMs is their sensitivity to assumed priors. Unfortunately, to-date a formal tool for sensitivity diagnostics in BHMs is missing. The project aims
to close this gap by developing and implementing a novel two-component sensitivity diagnostic tool for BHMs estimated by Stan, JAGS or INLA. A free accessible SEDA package in R will warn scientists about sensitivity issues in BHMs, supporting better self-control and model criticism. Two medical applications dealing with Breakpoints
for bacterial resistance and Material wear in 3D will answer questions relevant to microbiological and dental materials research.