Variational DAG Estimation via State Augmentation With Stochastic Permutations

no code implementations • 4 Feb 2024 • Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane

Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG), from observational data is a statistically and computationally hard problem with essential applications in areas such as causal discovery.

Causal Discovery Uncertainty Quantification +1