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graham-cleveland

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Bayesian Networks
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  1. Representation Probabilistic Graphical Models Independencies BayesianNetworks

  2. Independence & Factorization • Factorization of a distribution P implies independencies that hold in P • If P factorizes over G, can we read these independencies from the structure of G? P(X,Y) = P(X) P(Y) X,Y independent • P(X,Y,Z)  1(X,Z) 2(Y,Z) (X  Y | Z)

  3. Flow of influence & d-separation Definition: X and Y are d-separated in G given Z if there is no active trail in G between X and Y given Z D I G S L Notation: d-sepG(X, Y | Z)

  4. Factorization  Independence: BNs Theorem: If P factorizes over G, and d-sepG(X, Y | Z) then P satisfies (X  Y | Z) D I G S L

  5. Any node is d-separated from its non-descendants given its parents Coherence Difficulty Intelligence If P factorizes over G, then in P, any variable is independent of its non-descendants given its parents Grade SAT Letter Job Happy

  6. I-maps • d-separation in G  P satisfies corresponding independence statement • Definition: If P satisfies I(G), we say that G is an I-map (independency map) of P I(G) = {(X  Y | Z) : d-sepG(X, Y | Z)}

  7. I-maps P2 P1 G1 G2 D I D I

  8. Factorization  Independence: BNs Theorem: If P factorizes over G, then G is an I-map for P

  9. Independence  Factorization Theorem: If G is an I-map for P, then P factorizes over G D I G S L

  10. Summary Two equivalent views of graph structure: • Factorization: G allows P to be represented • I-map: Independencies encoded by G hold in P If P factorizes over a graph G, we can read from the graph independencies that must hold in P (an independency map)