Title: Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA,
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
(this http URL).