Factored Performance Functions with Structural Representation in Continuous Time Bayesian Networks
Liessman E. Sturlaugson, John W. Sheppard · 2014
The continuous time Bayesian network (CTBN) is a probabilistic graphical model that enables reasoning about complex, interdependent, and continuous-time subsystems. The model uses nodes to denote subsys-tems and arcs to denote conditional dependence. This dependence manifests in how the dynamics of a sub-system change based on the current states of its parents in the network. While the original CTBN definition al-lows users to specify the dynamics of how the system evolves, users might also want to place value expres-sions over the dynamics of the model in the form of per-formance functions. We formalize these performance functions for the CTBN and show how they can be fac-tored in the same way as the network, allowing what we argue is a more intuitive and explicit representation. For cases in which a performance function must involve multiple nodes, we show how to augment the structure of the CTBN to account for the performance interaction while maintaining the factorization of a single perfor-mance function for each node.