Abstract
Programs that learn Bayesian networks normally learn directed acyclic graphs (DAGs) of arbitrary structure, including those with repeating structures, such as dynamic Bayesian networks (DBNs). Perhaps for that reason there is relatively little literature on learning DBNs specifically and more focusing on applying general learners to the task. Here we modify a general causal discovery program to search specifically for dynamic Bayesian networks, and we identify the benefits in the quality of the models discovered and the time taken to discover them.
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