Abstract
The problem of efficient Bayesian computation in the context of linear Gaussian directed acyclic graph models is examined. Unobserved latent variables are grouped together in a block, and sparse matrix techniques for computation are explored. Conditional sampling and likelihood computations are shown to be straightforward using a sparse matrix approach, allowing Markov chain Monte Carlo algorithms with good mixing properties to be developed for problems with many thousands of latent variables.
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