Abstract
Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.
Keywords
- Uncertainty in AI: Bayesian Networks
- Multidisciplinary Topics and Applications: Finance
- Multidisciplinary Topics and Applications: Biology and Medicine
