Abstract
In this paper, we present an approach to build the structure of a Bayesian network from multiple disparate inputs. Specifically, our method accepts as input multiple partially overlapping datasets with missing data along with expert opinions about the structure of the model and produces an associated directed acyclic graph representing the graphical layer of a Bayesian network. We provide experimental results where we compare our algorithm with an application of Structural Expectation Maximization. We also provide a real world example motivating the need for combining disparate sources of information even when noisy and not fully aligned with one another.
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Sajja, S., Deleris, L.A. (2015). Bayesian Network Structure Learning with Messy Inputs: The Case of Multiple Incomplete Datasets and Expert Opinions. In: Walsh, T. (eds) Algorithmic Decision Theory. ADT 2015. Lecture Notes in Computer Science(), vol 9346. Springer, Cham. https://doi.org/10.1007/978-3-319-23114-3_8
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DOI: https://doi.org/10.1007/978-3-319-23114-3_8
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