Bayesian Inference for Sparse VAR(1) Models, with Application to Time Course Microarray Data
- *Corresponding Author:
- Richard J. Boys
School of Mathematics & Statistics
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 scenarioâ€™â€™. 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.