Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2017-10-16
Print Publication Date2017
Permanent link to this recordhttp://hdl.handle.net/10754/626186
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AbstractBayesian 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).
CitationFerkingstad E, Held L, Rue H (2017) Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA. Stat 6: 331–344. Available: http://dx.doi.org/10.1002/sta4.163.
SponsorsWe thank Anne Presanis for providing code and documentation for an MCMC implementation of the “rats” example. Thanks are also due to Lorenz Wernisch and Robert Goudie for helpful comments.