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A sparse matrix approach to Bayesian computation in large linear models

Lookup NU author(s): Professor Darren Wilkinson, Stephen Yeung

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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. © 2003 Elsevier B.V. All rights reserved.


Publication metadata

Author(s): Wilkinson DJ, Yeung SKH

Publication type: Article

Publication status: Published

Journal: Computational Statistics and Data Analysis

Year: 2004

Volume: 44

Issue: 3

Pages: 493-516

ISSN (print): 0167-9473

ISSN (electronic): 1872-7352

Publisher: Elsevier

URL: .http;//dx.doi.org/10.1016/S0167-9473(02)00252-9

DOI: 10.1016/S0167-9473(02)00252-9


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