1 / 6

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.

tiana
Télécharger la présentation

Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related