ePrints

A sparse matrix approach to Bayesian computation in large linear models

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

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

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

Altmetrics

Share