1. Representation
Probabilistic
Graphical
Models
Local Structure
Overview

2. Tabular Representations
0.3
0.08
0.25
0.4 g2
0.02
0.9
i1,d0
0.7
0.05
i0,d1
0.5 g1 g3
0.2
i1,d1
i0,d0
Pneu-
monia
Flu TB
Bron-
chitis
Cough
k parents, 2^k params

3. General CPD
CPD P(X | Y1, …, Yk) specifies distribution over X for each assignment y1, …, 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
Example: OR, AND

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