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- W4293406341 abstract "Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidate structures using their posterior probabilities for a given data set. Score-based algorithms then use those posterior probabilities as an objective function and return the maximum a posteriori network as the learned model. For discrete Bayesian networks, the canonical choice for a posterior score is the Bayesian Dirichlet equivalent uniform (BDeu) marginal likelihood with a uniform (U) graph prior (Heckerman et al., 1995). Its favourable theoretical properties descend from assuming a uniform prior both on the space of the network structures and on the space of the parameters of the network. In this paper, we revisit the limitations of these assumptions; and we introduce an alternative set of assumptions and the resulting score: the Bayesian Dirichlet sparse (BDs) empirical Bayes marginal likelihood with a marginal uniform (MU) graph prior. We evaluate its performance in an extensive simulation study, showing that MU+BDs is more accurate than U+BDeu both in learning the structure of the network and in predicting new observations, while not being computationally more complex to estimate." @default.
- W4293406341 created "2022-08-29" @default.
- W4293406341 creator A5059820079 @default.
- W4293406341 date "2016-05-12" @default.
- W4293406341 modified "2023-09-27" @default.
- W4293406341 title "An Empirical-Bayes Score for Discrete Bayesian Networks" @default.
- W4293406341 doi "https://doi.org/10.48550/arxiv.1605.03884" @default.
- W4293406341 hasPublicationYear "2016" @default.
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