Time-Varying Dynamic Bayesian Networks

By Le Song, Mladen Kolar, Eric P. Xing. 2009

219 citations recorded by the source corpus.

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Citations in the corpus

Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “Several methods aim to model time-varying time-delayed causal relations (Xing et al., 2010; Song et al., 2009), which can be reduced to online parameter learning because the direction of causal relations is given (i.”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “Several methods aim to model time-varying time-delayed causal relations [Xing et al. , 2010; Song et al. , 2009], which can be reduced to online parameter learning because the direction of the causal relations is given (i.e., the past inﬂuences the future).”
DYNOTEARS: Structure Learning from Time-Series Data (2020, background): “It would be useful to relax these in various ways, for example by allowing the structure to change smoothly over time (Song et al., 2009) or at discrete change points that we infer from the data (Grzegorczyk and Husmeier, 2011).”
Joint estimation of multiple networks from time course data (2013, background): “The ideas discussed here share many connections with time-heterogeneous DBNs which, for brevity, we did not discuss in this paper (Dondelinger et al., 2010, 2012; Grzegorczyk and Husmeier, 2011; Song et al., 2009).”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “…Somogyi 1998; Murphy and Mian 1999; Akutsu, Miyano, and Kuhara 2000; Friedman et al. 2000; Hartemink et al. 2001; Shmulevich et al. 2002; Zou and Conzen 2005; Gupta, Qu, and Ibrahim 2007; Martin et al. 2007; Hirose et al. 2008; Kojima et al. 2009; Shimamura et al. 2009; Song, Kolar, and Xing 2009).”
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models (2019, background): “For the latter, it includes research studies in dynamic Bayesian networks (Dagum et al., 1992; Song et al., 2009).”
The joint graphical lasso for inverse covariance estimation across multiple classes (2011, methodology): “Some proposals have used time-series data to deﬁne time-varying networks in the context of continuous or binary data (Zhou et al. 2008, Song et al. 2009 a , Ahmed & Xing 2009, Kolar & Xing 2009, Song et al. 2009 b , Kolar et al. 2010).”
Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “Several methods aim to model time-varying time-delayed causal relations [16, 17], which can be reduced to online parameter learning because the direction of the causal relations is given (i.e., the past inﬂu-ences the future).”