Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA

TL;DR

This paper introduces a fast, accurate method for Bayesian model criticism and conflict diagnostics in hierarchical models using R-INLA, enabling efficient detection of outliers and conflicts among groups.

Contribution

The authors develop a novel approach for group-level model criticism and conflict detection using INLA, significantly reducing computational demands compared to MCMC methods.

Findings

Method is computationally efficient and accurate

Implemented as part of the R-INLA package

Enables practical conflict diagnostics in complex models

Abstract

Bayesian hierarchical models are increasingly popular for realistic modelling and analysis of complex data. This trend is accompanied by the need for flexible, general, and computationally efficient methods for model criticism and conflict detection. Usually, a Bayesian hierarchical model incorporates a grouping of the individual data points, for example individuals in repeated measurement data. In such cases, the following question arises: Are any of the groups "outliers", or in conflict with the remaining groups? Existing general approaches aiming to answer such questions tend to be extremely computationally demanding when model fitting is based on MCMC. We show how group-level model criticism and conflict detection can be done quickly and accurately through integrated nested Laplace approximations (INLA). The new method is implemented as a part of the open source R-INLA package for Bayesian computing (http://r-inla.org).