Network structure learning algorithms have aided net-work discovery in fields such as bioinformatics, neu-roscience, ecology and social science. However, chal-lenges remain in learning informative networks for re-lated sets of tasks because the search space of Bayesian network structures is characterized by large basins of approximately equivalent solutions. Multitask algo-rithms select a set of networks that are near each other in the search space, rather than a score-equivalent set of networks chosen from independent regions of the space. This selection preference allows a domain ex-pert to see only differences supported by the data. How-ever, the usefulness of these algorithms for scientific datasets is limited because existing algorithms naively assume that all pairs of tasks are equally related. We introduce a framework that relaxes this assumption by incorporating domain knowledge about task-relatedness into the learning objective. Using our framework, we in-troduce the first multitask Bayesian network algorithm that leverages domain knowledge about the relatedness of tasks. We use our algorithm to explore the effect of task-relatedness on network discovery and show that our algorithm learns networks that are closer to ground truth than naive algorithms and that our algorithm dis-covers patterns that are interesting.

This dissertation is approved, and it is acceptable in quality and form for publication: Approved by the Dissertation Committee: