Bayesian inference for sparse VAR(1) models, with application to time course microarray data

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.