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AN ALGORITHMIC APPROACH TO BAYESIAN LINEAR MODEL CALCULATIONS
Author(s) -
Carlin J.B.
Publication year - 1990
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1990.tb00997.x
Subject(s) - linear model , computer science , computation , algorithm , covariance , linear map , generalized linear mixed model , covariance matrix , bayesian probability , theoretical computer science , mathematics , machine learning , artificial intelligence , statistics , pure mathematics
Summary A framework is described for organizing and understanding the computations necessary to obtain the posterior mean of a vector of linear effects in a normal linear model, conditional on the parameters that determine covariance structure. The approach has two major uses; firstly, as a pedagogical tool in the derivation of formulae, and secondly, as a practical tool for developing computational strategies without needing complicated matrix formulae that are often unwieldy in complex hierarchical models. The proposed technique is based upon symbolic application of the sweep operator SWP to an appropriate tableau of means and covariances. The method is illustrated with standard linear model specifications, including the so‐called mixed model, with both fixed and random effects.