Time-Varying Dynamic Bayesian Networks

Directed graphical models such as Bayesian networks are a favored formalism for modeling the dependency structures in complex multivariate systems such as those encountered in biology and neural science. When a system is undergo-ing dynamic transformation, temporally rewiring networks are needed for cap-turing the dynamic causal influences between covariates. In this paper, we pro-pose time-varying dynamic Bayesian networks (TV-DBN) for modeling the struc-turally varying directed dependency structures underlying non-stationary biologi-cal/neural time series. This is a challenging problem due the non-stationarity and sample scarcity of time series data. We present a kernel reweighted `1-regularized auto-regressive procedure for this problem which enjoys nice properties such as computational efficiency and provable asymptotic consistency. To our knowledge, this is the first practical and statistically sound method for structure learning of TV-DBNs. We applied TV-DBNs to time series measurements during yeast cell cycle and brain response to visual stimuli. In both cases, TV-DBNs reveal interesting dynamics underlying the respective biological systems.