Dondelinger, F., Lebre, S. and Husmeier, D.
In "Proceedings of the International Conference on Machine Learning (ICML 2010)", 303-310. Eds. Furnkranz J. and Joachims T.. Omnipress, Madison, Wisconsin, USA..
Omnipress, Madison, Wisconsin, USA.
Network inference, dynamic Bayesian networks, changepoints, reversible jump Markov chain Monte Carlo, gene regulation, gene expression time series, morphogenesis
||Classical dynamic Bayesian networks (DBNs) are based on the
homogeneous Markov assumption and cannot deal with
heterogeneity and non-stationarity in temporal processes.
Various approaches to relax the homogeneity assumption
have recently been proposed. The present paper aims to
improve the shortcomings of three recent versions
of heterogeneous DBNs along the following lines:
(i) avoiding the need for data discretization,
(ii) increasing the flexibility over a
time-invariant network structure,
(iii) avoiding over-flexibility and overfitting by introducing
a regularization scheme based in inter-time segment information
sharing. The improved method is evaluated on synthetic data
and compared with alternative published methods on gene expression
time series from Drosophila melanogaster.