1 / 35

Lecture 11 Slides May 2 nd , 2006

University of Washington Department of Electrical Engineering EE512 Spring, 2006 Graphical Models Jeff A. Bilmes <bilmes@ee.washington.edu>. Lecture 11 Slides May 2 nd , 2006. Announcements. READING: M. Jordan: Chapters 4,10,12,17,18 Reminder: TA discussions and office hours:

fred
Télécharger la présentation

Lecture 11 Slides May 2 nd , 2006

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. University of WashingtonDepartment of Electrical Engineering EE512 Spring, 2006 Graphical ModelsJeff A. Bilmes <bilmes@ee.washington.edu> Lecture 11 Slides May 2nd, 2006 EE512 - Graphical Models - J. Bilmes

  2. Announcements • READING: • M. Jordan: Chapters 4,10,12,17,18 • Reminder: TA discussions and office hours: • Office hours: Thursdays 3:30-4:30, Sieg Ground Floor Tutorial Center • Discussion Sections: Fridays 9:30-10:30, Sieg Ground Floor Tutorial Center Lecture Room • Reminder: take-home Midterm: May 5th-12th, you must work alone on this. • Note: I am gone all next week (May 8th-12th). Subsequent weeks we will have makeup classes on Mondays at 6:00pm. EE512 - Graphical Models - J. Bilmes

  3. Class Road Map • L1: Tues, 3/28: Overview, GMs, Intro BNs. • L2: Thur, 3/30: semantics of BNs + UGMs • L3: Tues, 4/4: elimination, probs, chordal I • L4: Thur, 4/6: chrdal, sep, decomp, elim • L5: Tue, 4/11: chdl/elim, mcs, triang, ci props. • L6: Thur, 4/13: MST,CI axioms, Markov prps. • L7: Tues, 4/18: Mobius, HC-thm, (F)=(G) • L8: Thur, 4/20: phylogenetic trees, HMMs • L9: Tue, 4/25: HMMs, inference on trees • L10: Thur, 4/27: Inference on trees, start poly • L11: Tues, 5/2: polytrees, start JT inference • L12: Thur, 5/4 • L13: Tues, 5/9 • L14: Thur, 5/11 • L15: Tue, 5/16 • L16: Thur, 5/18 • L17: Tues, 5/23 • L18: Thur, 5/25 • L19: Tue, 5/30 • L20: Thur, 6/1: final presentations EE512 - Graphical Models - J. Bilmes

  4. Final Project Milestone Due Dates • L1: Tues, 3/28: • L2: Thur, 3/30: • L3: Tues, 4/4: • L4: Thur, 4/6: • L5: Tue, 4/11: • L6: Thur, 4/13: • L7: Tues, 4/18: • L8: Thur, 4/20: Team Lists, short abstracts I • L9: Tue, 4/25: • L10: Thur, 4/27: short abstracts II • L11: Tues, 5/2: today • L12: Thur, 5/4: abstract II + progress • L13: Tues, 5/9 • L14: Thur, 5/11: 1 page progress report • L15: Tue, 5/16 • L16: Thur, 5/18: 1 page progress report • L17: Tues, 5/23 • L18: Thur, 5/25: 1 page progress report • L19: Tue, 5/30 • L20: Thur, 6/1: final presentations • L21: Tue, 6/6 4-page papers due (like a conference paper). • Team lists, abstracts, and progress reports must be turned in, in class and using paper (dead tree versions only). • Final reports must be turned in electronically in PDF (no other formats accepted). • Progress reports must report who did what so far!! EE512 - Graphical Models - J. Bilmes

  5. Summary of Last Time • Inference on trees • Inference on undirected trees • Example: voting tallying by message passing in trees • Begin inference on poly trees EE512 - Graphical Models - J. Bilmes

  6. Outline of Today’s Lecture • inference on poly trees • Begin exact inference on junction trees EE512 - Graphical Models - J. Bilmes

  7. Books and Sources for Today • M. Jordan: Chapters 4,10,12,17,18 • J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988. EE512 - Graphical Models - J. Bilmes

  8. Bottom up Inference in undirected trees EE512 - Graphical Models - J. Bilmes

  9. Inference in undirected trees EE512 - Graphical Models - J. Bilmes

  10. Inference in undirected trees EE512 - Graphical Models - J. Bilmes

  11. Inference in undirected trees EE512 - Graphical Models - J. Bilmes

  12. Inference in undirected trees EE512 - Graphical Models - J. Bilmes

  13. Inference in polytrees EE512 - Graphical Models - J. Bilmes

  14. Inference in polytrees EE512 - Graphical Models - J. Bilmes

  15. Required Messages EE512 - Graphical Models - J. Bilmes

  16. Inference in polytrees EE512 - Graphical Models - J. Bilmes

  17. Inference in polytrees EE512 - Graphical Models - J. Bilmes

  18. Required Messages EE512 - Graphical Models - J. Bilmes

  19. Inference in polytrees EE512 - Graphical Models - J. Bilmes

  20. Inference in polytrees EE512 - Graphical Models - J. Bilmes

  21. Inference in polytrees: summary EE512 - Graphical Models - J. Bilmes

  22. What if cycles exist? EE512 - Graphical Models - J. Bilmes

  23. Ex: messages not valid EE512 - Graphical Models - J. Bilmes

  24. Inference EE512 - Graphical Models - J. Bilmes

  25. Inference: outline EE512 - Graphical Models - J. Bilmes

  26. Inference: moralization EE512 - Graphical Models - J. Bilmes

  27. Examples: assigning prob to clique potentials EE512 - Graphical Models - J. Bilmes

  28. Examples: assigning prob to clique potentials EE512 - Graphical Models - J. Bilmes

  29. Evidence EE512 - Graphical Models - J. Bilmes

  30. Goal calculation & Evidence EE512 - Graphical Models - J. Bilmes

  31. Example potentials EE512 - Graphical Models - J. Bilmes

  32. Goal EE512 - Graphical Models - J. Bilmes

  33. Potentials as marginals EE512 - Graphical Models - J. Bilmes

  34. Potentials don’t start as marginals EE512 - Graphical Models - J. Bilmes

  35. Potentials don’t start as marginals EE512 - Graphical Models - J. Bilmes

More Related