dBN Model

Dynamic Bayesian Networks (DBNs) are probabilistic graphical models used to represent and learn temporal relationships between variables, primarily focusing on predicting future states or outcomes based on observed data. Current research emphasizes efficient algorithms for learning DBN structures and parameters from data, including adaptations of Newton methods and evolutionary algorithms, as well as exploring their application in diverse fields such as healthcare (predicting acute kidney injury), finance (stock market forecasting), and cybersecurity (intrusion detection). The ability of DBNs to model complex temporal dependencies and handle uncertainty makes them a valuable tool for various applications requiring predictive modeling and decision support.