alexa Bayesian Inference for Sparse VAR(1) Models, with Application to Time Course Microarray Data
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Bayesian Inference for Sparse VAR(1) Models, with Application to Time Course Microarray Data

Guiyuan Lei1,2, Richard J Boys1,2*, Colin S Gillespie1,2, Amanda Greenall2,3 and Darren J Wilkinson1,2

1School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.

2Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN), Newcastle University

3Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK

*Corresponding Author:
Richard J. Boys
School of Mathematics & Statistics
Newcastle University
Newcastle upon Tyne, NE1 7RU, UK
E-mail: [email protected]

Received Date: October 22, 2011; Accepted Date: November 21, 2011; Published Date: December 25, 2011

Citation: Lei G, Boys RJ, Gillespie CS, Greenall A, Wilkinson DJ (2011) Bayesian Inference for Sparse VAR(1) Models, with Application to Time Course Microarray Data. J Biomet Biostat 2:127. doi: 10.4172/2155-6180.1000127

Copyright: © 2011 Lei G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



This paper considers the problem of undertaking fully Bayesian inference for both the parameters and structure of a vector autoregressive model on the basis of time course data in the ``p>> n scenario’’. The autoregressive matrix is assumed to be sparse, but of unknown structure. The resulting algorithm for dynamic Bayesian network inference is shown to be highly effective, and is applied to the problem of dynamic network inference from time course microarray data using a dataset concerned with the transient response of budding yeast to telomere damage.


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