Workload-Aware Materialization of Junction Trees

@inproceedings{Ciaperoni2022WorkloadAwareMO,
  title={Workload-Aware Materialization of Junction Trees},
  author={Martino Ciaperoni and Γ‡igdem Aslay and A. Gionis and Michael Mathioudakis},
  booktitle={EDBT},
  year={2022}
}
Bayesian networks are popular probabilistic models that capture the conditional dependencies among a set of variables. Inference in Bayesian networks is a fundamental task for answering probabilistic queries over a subset of variables in the data. However, exact inference in Bayesian networks is NP-hard, which has prompted the development of many practical inference methods. In this paper, we focus on improving the performance of the junction-tree algorithm, a well-known method for exact… 

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