Abstract
This paper introduces the notion of Bayesian networks augmented with function nodes. Two types of function nodes are considered. A real-valued function node represents a real value either used to parameterise one or more conditional probability distributions of the Bayesian network or a real value computed after a successful belief update or Monte Carlo simulation. On the other hand, a discrete function node represents a discrete marginal distribution. The paper includes four real-world examples that illustrate how function nodes have improved the flexibility and efficiency of utilizing Bayesian networks for reasoning with uncertainty in different domains.
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Madsen, A.L., Jensen, F., Karlsen, M., Soendberg-Jeppesen, N. (2014). Bayesian Networks with Function Nodes. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_19
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DOI: https://doi.org/10.1007/978-3-319-11433-0_19
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