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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. Pneu - monia. Flu. TB. Bron - chitis. Cough.

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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 Pneu- monia Flu TB Bron- chitis Cough

  3. General CPD • CPD P(X | Y1, …, Yk) specifies distribution over X for each assignmenty1, …, yk • Can use any function to specify a factor (X, Y1, …, Yk) such that ∑x(x, y1, …, yk) = 1 for all y1, …, yk

  4. Many Models • Deterministic CPDs • Tree-structured CPDs • Logistic CPDs & generalizations • Noisy OR / AND • Linear Gaussians & generalizations

  5. Context-Specific Independence

  6. 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

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