7 papers
Mechanisms and Pathways of Extreme Events in Partially-Observed Stochastic Dynamical Systems
Charlotte Moser, Nan Chen, Marios Andreou
Extreme events occur across the natural, engineering, and socioeconomic sciences, where rare but high-impact episodes can lead to disproportionate consequences that pose major chal…
A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
Zhang Jiang, Marios Andreou, Sebastian Reich +1
Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online for…
Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference
Marios Andreou, Nan Chen
Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links, a recently developed method, assimil…
Assimilative Causal Inference
Marios Andreou, Nan Chen, Erik Bollt
Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimension…
An Adaptive Online Smoother with Closed-Form Solutions and Information-Theoretic Lag Selection for Conditional Gaussian Nonlinear Systems
Marios Andreou, Nan Chen, Yingda Li
Data assimilation (DA) combines partial observations with dynamical models to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for…
A Martingale-Free Introduction to Conditional Gaussian Nonlinear Systems
Marios Andreou, Nan Chen
The conditional Gaussian nonlinear system (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining f…