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Time-Varying Dynamic Bayesian Networks
Le Song · Mladen Kolar · Eric Xing

Tue Dec 08 05:19 PM -- 05:20 PM (PST) @ None
Directed graphical models such as Bayesian networks are a favored formalism to model the dependency structures in complex multivariate systems such as those encountered in biology and neural sciences. When the system is undergoing dynamic transformation, often a temporally rewiring network is needed for capturing the dynamic causal influences between covariates. In this paper, we propose a time-varying dynamic Bayesian network (TV-DBN) for modeling the structurally varying directed dependency structures underlying non-stationary biological/neural time series. This is a challenging problem due the non-stationarity and sample scarcity of the time series. We present a kernel reweighted $\ell_1$ regularized auto-regressive procedure for learning the TV-DBN model. Our method enjoys nice properties such as computational efficiency and provable asymptotic consistency. Applying TV-DBN to time series measurements during yeast cell cycle and brain response to visual stimuli reveals interesting dynamics underlying the respective biological systems.

Author Information

Le Song (Carnegie Mellon University)
Mladen Kolar (University of Chicago)
Eric Xing (Petuum Inc. / Carnegie Mellon University)

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