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

Egil Ferkingstad, Leonhard Held, Haavard Rue

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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, as, for example, with 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 Markov chain Monte Carlo. We show how group-level model criticism and conflict detection can be carried out 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).
Original languageEnglish (US)
Pages (from-to)331-344
Number of pages14
JournalStat
Volume6
Issue number1
DOIs
StatePublished - Oct 16 2017

Fingerprint

Dive into the research topics of 'Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA'. Together they form a unique fingerprint.

Cite this