πArXiv AIβ’Stalecollected in 17h
FPT Bayesian Nets via Feedback Edges
β‘ 30-Second TL;DR
What Changed
Feedback edge set FPT
Why It Matters
Provides theoretical foundations for efficient BNSL algorithms under graph parameters. Enables better scalability in causal discovery applications.
What To Do Next
Evaluate benchmark claims against your own use cases before adoption.
Who should care:Researchers & Academics
Key Points
- β’Feedback edge set FPT
- β’Treewidth FPT with additive input
- β’Complexity classification
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Original source: ArXiv AI β
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