independence in markov networks
DESCRIPTION
Representation. Probabilistic Graphical Models. Markov Networks. Independence in Markov Networks. Influence Flow in Undirected Graph. Separation in Undirected Graph. A trail X 1 —X 2 — … — X k-1 — X k is active given Z X and Y are separated in H given Z if. - PowerPoint PPT PresentationTRANSCRIPT
Daphne Koller
Markov Networks
Independencein Markov Networks
ProbabilisticGraphicalModels
Representation
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Influence Flow in Undirected Graph
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Separation in Undirected Graph
• A trail X1—X2—… —Xk-1—Xk is active given Z
• X and Y are separated in H given Z if
Daphne Koller
Independences in Undirected Graph
• The independences implied by H
I(H) =
• We say that H is an I-map (independence map) of P if
Daphne Koller
FactorizationP factorizes over H
Daphne Koller
Factorization Independence
Theorem: If P factorizes over H then H is an I-map for P
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BD
C
A
E
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Independence Factorization
Theorem: If H is an I-map for P then P factorizes over H
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Independence Factorization
Hammersley-Clifford Theorem: If H is an I-map for P, and P is positive, then P factorizes over H
Daphne Koller
Summary• Separation in Markov network H allows us to
“read off” independence properties that hold in any Gibbs distribution that factorizes over H
• Although the same graph can correspond to different factorizations, they have the same independence properties