Inferring temporal dynamics from cross-sectional data using Langevin dynamics

• Main Authors: Dutta, Pritha, Quax, Rick, Crielaard, Loes, Badiali, Luca, Sloot, Peter M. A.
• Other Authors: Interdisciplinary Graduate School (IGS)
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Cross-Sectional Data; Predictive Computational Models
• Online Access: https://hdl.handle.net/10356/159990
• Institution: Nanyang Technological University

Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying dynamics. This temporal information is essential to develop predictiv...