# 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.