Bayesian networks for large and complex domains are difficult to construct and maintain. For example modifying a small network fragment in a repetitive structure might be very time consuming. Top-down modelling may simplify the construction of large Bayesian networks, but methods (partly) supporting top-down modelling have only recently been introduced and tools do not exist. In this paper, we ...

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algor...