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
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
Plot functionalities are provided via the
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.