Bayesian Structure Learning with Generative Flow Networks
What this work does
Constructs DAGs sequentially with a generative flow network trained to sample graphs proportionally to Bayesian scores rather than returning only a maximizing graph.
Connection to discovery
An alternative to reward maximization when discovery requires diverse plausible hypotheses and uncertainty-aware experimental design.
Evidence
Inspected the detailed-balance setup, acyclicity masks and posterior-feature/graph comparisons.
Scope and limitations
The demonstrated Bayesian-network setting assumes fully observed iid data and usable modular scores. Posterior graph diversity does not itself prove causal identifiability.
THE RESEARCH CONVERSATION
Discussion & reactions
Share a question, an insight, or a replication experience. Sign in with GitHub to join the conversation.