Time-Varying Dynamic Bayesian Networks
Time-Varying Dynamic Bayesian Networks
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发表时间：
2009-12
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通讯作者：
Le Song;M. Kolar;E. Xing
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作者：
Le Song;M. Kolar;E. Xing
诸如贝叶斯网络之类的定向图形模型是对复杂多元系统（例如生物学和神经科学中遇到的依赖性结构）进行建模的偏爱形式主义。当系统进行动态转换时，需要时间重新布线网络来捕获协变量之间的动态因果影响。在本文中，我们提出了时间变化的动态贝叶斯网络（TV-DBN），用于建模非平稳生物/神经时间序列的结构变化的定向依赖性结构。由于时间序列数据的非平稳性和样本稀缺性，这是一个具有挑战性的问题。我们为此问题提供了一个内核重新释放的L1调查自动回归程序，该程序具有诸如计算效率和可证明的渐近一致性之类的良好属性。据我们所知，这是第一种实用且统计上合理的方法，用于构建电视型型型型型电视。我们在酵母细胞周期和大脑对视觉刺激的反应期间将电视-DBN应用于时间序列测量。在这两种情况下，TV-DBN都揭示了各自的生物系统基础的有趣动态。
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 undergoing dynamic transformation, temporally rewiring networks are needed for capturing the dynamic causal influences between covariates. In this paper, we propose time-varying dynamic Bayesian networks (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 time series data. We present a kernel reweighted l1-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.