Empirical Bayes Estimation of Dynamic Bayesian Networks.

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.