Estimating sparse gene regulatory networks using a bayesian linear regression.
We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive the minimum mean-square error (MMSE) estimate of the parameter vector and a computationally efficient approximation of it. We also derive an empirical-Bayesian version of the estimator, which does not need any a priori information, nor does it need the selection of any user parameters. As a byproduct, we obtain a powerful model ("basis") selection tool for sparse models. The performance and robustness of our new estimators are illustrated via numerical examples