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

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @
in Poster Session »
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.
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Author Information

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

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