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Article overview
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Gibbs Sampling for a Bayesian Hierarchical Version of the General Linear Mixed Model | | Alicia A. Johnson
; Galin L. Jones
; | | Date: |
18 Dec 2007 | | Abstract: | We consider two-component block Gibbs sampling for a Bayesian hierarchical
version of the normal theory general linear mixed model. This model is
practically relevant in the sense that it is general enough to have many
applications and in that it is not straightforward to sample directly from the
corresponding posterior distribution. There are two possible orders in which to
update the components of our block Gibbs sampler. For both update orders, drift
and minorization conditions are constructed for the corresponding Markov
chains. Most importantly, these results establish geometric ergodicity for the
block Gibbs sampler. We also construct a minorization condition that will allow
practitioners to exploit regenerative simulation techniques for constructing a
reasonable initial distribution and constructing valid Monte Carlo standard
errors. | | Source: | arXiv, 0712.3056 | | Services: | Forum | Review | PDF | Favorites | |
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