Extends DiBS into VaMSL to infer mixtures of causal Bayesian networks, using Bayesian experimental design to elicit and incorporate expert priors, yielding improved structure learning on heterogeneous synthetic data and a breast cancer database.
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Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
Extends DiBS into VaMSL to infer mixtures of causal Bayesian networks, using Bayesian experimental design to elicit and incorporate expert priors, yielding improved structure learning on heterogeneous synthetic data and a breast cancer database.