AN ALGORITHMIC APPROACH TO BAYESIAN LINEAR MODEL CALCULATIONS
作者:
J.B. Carlin,
期刊:
Australian Journal of Statistics
(WILEY Available online 1990)
卷期:
Volume 32,
issue 1
页码: 29-43
ISSN:0004-9581
年代: 1990
DOI:10.1111/j.1467-842X.1990.tb00997.x
出版商: Blackwell Publishing Ltd
关键词: Posterior mean;mixed model;random effects;hierarchical normal linear model;covariance components;matrix identities;SWP operator
数据来源: WILEY
摘要:
SummaryA 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 effect
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