Tracking Time-varying Graphical Structure

24 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “Improved versions include the online change point detection method (Adams and Mackay, 2007), the online undirected graph learning (Talih and Hengartner, 2005), and the locally stationary structure tracker algorithm (Kummerfeld and Danks, 2013).”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “…[ Adams and Mackay, 2007 ] , the online undirected graph learning [Talih and Hengartner, 2005], the locally stationary structure tracker algorithm [Kummerfeld and Danks, 2013], and the regime aware learning algorithm to learn a sequence of Bayesian networks (BNs) that model a system with regime…”
Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “Improved versions include the online changepoint detection method [12], the online undirected graph learning [13], the locally stationary structure tracker algorithm [14].”
Causal Discovery and Inference: Concepts and Recent Methodological Advances (2016, methodology): “There exist some methods aiming to detect the changes [Talih and Hengartner (2005); Adams and Mackay 2007); Kummerfeld and Danks 2013)] or directly model time-varying causal relations (see, e.g., Huang et al. (2015)) in a dynamic manner.”