# ebdbNet: Empirical Bayes Estimation of Dynamic Bayesian Networks

Author: Andrea Rau

This package is used to infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

Posterior distributions (mean and variance) of network parameters are estimated using time-course data based on a linear feedback state space model that allows for a set of hidden states to be in- corporated. The algorithm is composed of three principal parts: choice of hidden state dimension (see `hankel`

), estimation of hidden states via the Kalman filter and smoother, and calculation of posterior distributions based on the empirical Bayes estimation of hyperparameters in a hierarchical Bayesian framework (see `ebdbn`

).

Plot functionalities are provided via the `igraph`

package.

### Reference

A. Rau, F. Jaffrezic, J.-L. Foulley, R. W. Doerge (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Statistical Applications in Genetics and Molecular Biology, vol. 9, iss. 1, article 9.