Reading Dependencies from Polytree-Like Bayesian Networks
arXiv:1206.5263v16 citations
Originality Synthesis-oriented
AI Analysis
This work addresses a specific theoretical problem in probabilistic graphical models, but it is incremental as it builds on existing assumptions and structures.
The authors tackled the problem of reading dependencies from polytree-like Bayesian networks by developing a graphical criterion for minimal directed independence maps, proving it is sound and complete under assumptions of composition and weak transitivity.
We present a graphical criterion for reading dependencies from the minimal directed independence map G of a graphoid p when G is a polytree and p satisfies composition and weak transitivity. We prove that the criterion is sound and complete. We argue that assuming composition and weak transitivity is not too restrictive.