Review of Bayesian Regression Modelling with INLA by Xiaofeng Wang, Yu Ryan Yue, and Julian J. Faraway
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For many statisticians, the use of Bayesian methods implies a journey into all of the associated challenges of writing bespoke MCMC code. Perhaps worse, for applied statistical practitioners, Bayesian methods imply the use of ancient, unsupported WinBUGS or OpenBUGS software. Newer implementations such as JAGS and Stan have alleviated significant amounts of suffering, but since its 2009 seminal paper Rue et al. (2009), INLA has broken the synonymy of Bayes and MCMC. Adoption has been highest in fields where computational challenges are the most severe, notably spatial and temporal modelling.
In Bayesian Regression Modelling with INLA, Wang, Yue and Faraway deliver a first, much-needed general text on INLA that is not concentrated on spatial modelling. They begin with a practical refresher on Bayesian inference, sufficient for anyone rusty but not a substitute for original study, for which they refer the audience to fundamental resources like Gelman’s BDA Gelman et al. (2013). For any...
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