Abstract

Conventional dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption, which is too restrictive in many practical applications. Various approaches to relax the homogeneity assumption have recently been proposed, allowing the network structure to change with time. However, unless time series are very long, this flexibility leads to the risk of overfitting and inflated inference uncertainty. In the present paper we investigate three regularization schemes based on inter-segment information sharing, choosing different prior distributions and different coupling schemes between nodes. We apply our method to gene expression time series obtained during the Drosophila life cycle, and compare the predicted segmentation with other state-of-the-art techniques. We conclude our evaluation with an application to synthetic biology, where the objective is to predict a known in vivo regulatory network of five genes in yeast.