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Lookup NU author(s): Professor Darren Wilkinson,
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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. © 2003 Elsevier B.V. All rights reserved.
Author(s): Wilkinson DJ, Yeung SKH
Publication type: Article
Publication status: Published
Journal: Computational Statistics and Data Analysis
ISSN (print): 0167-9473
ISSN (electronic): 1872-7352
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