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Overview

Representation. Probabilistic Graphical Models. Local Structure. Overview. g 1. g 2. g 3. i 0 ,d 0. 0.3. 0.4. 0.3. i 0 ,d 1. 0.05. 0.25. 0.7. i 1 ,d 0. 0.9. 0.08. 0.02. i 1 ,d 1. 0.5. 0.3. 0.2. Tabular Representations. General CPD. Many Models. Deterministic CPDs

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Overview

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  1. Representation Probabilistic Graphical Models Local Structure Overview

  2. g1 g2 g3 i0,d0 0.3 0.4 0.3 i0,d1 0.05 0.25 0.7 i1,d0 0.9 0.08 0.02 i1,d1 0.5 0.3 0.2 Tabular Representations

  3. General CPD

  4. Many Models • Deterministic CPDs • Context-specific CPDs (trees, rules) • Logistic CPDs & generalizations • Noisy OR / AND • Linear Gaussians & generalizations

  5. General Factors: Log-linear Model

  6. Context-Specific Independence

  7. Y1 Y2 Which of the following context-specific independences hold when X is a deterministic OR of Y1 and Y2? (Mark all that apply.) X

  8. END END END

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