Lec 18: May 31st, 2006EE512 - Graphical Models - J. BilmesPage 1 Jeff A. Bilmes University of...
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Transcript of Lec 18: May 31st, 2006EE512 - Graphical Models - J. BilmesPage 1 Jeff A. Bilmes University of...
Lec 18: May 31st, 2006 EE512 - Graphical Models - J. Bilmes Page 1
University of WashingtonDepartment of Electrical Engineering
EE512 Spring, 2006 Graphical Models
Jeff A. Bilmes <[email protected]>Jeff A. Bilmes <[email protected]>
Lecture 18 Slides
May 31st, 2006
Lec 18: May 31st, 2006 EE512 - Graphical Models - J. Bilmes Page 2
• READING: – Google search on “bayesian networks” “approximate
inference”
• No more homework this quarter, concentrate on final projects!!
• Final project presentations tomorrow in class!!
Announcements
Lec 18: May 31st, 2006 EE512 - Graphical Models - J. Bilmes Page 3
• 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: Inference in JTs• Tues, 5/9: away• Thur, 5/11: away• L13: Tue, 5/16: JT, GDL, Shenoy-Schafer• L14: Thur, 5/18: GDL, Search, Gaussians I• L--: Mon, 5/22: laptop crash • L15: Tues, 5/23: search, Gaussians I• L16: Thur, 5/25: Gaussians• Mon, 5/29: Holiday• L17: Tue, 5/30• L18: Wed, 5/31• L19: Thur, 6/1: final presentations
• L20: Tue, 6/6
Class Road Map
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• 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:
• L12: Thur, 5/4: abstract II + progress• L--: Tues, 5/9• L--: Thur, 5/11: 1 page progress report• L13: Tue, 5/16:
• L14: Thur, 5/18: 1 page progress report• L15: Tues, 5/23• L16: Thur, 5/25: 1 page progress report• L17: Tue, 5/30: Today• L18: Wed, 5/31:• L19: Thur, 6/1: final presentations
• L20: Tue, 6/6 4-page papers due (like a conference paper), Only .pdf versions accepted.
Final Project Milestone Due Dates
• 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).
• No need to repeat what was on previous progress reports/abstracts, I have those available to refer to.
• Progress reports must report who did what so far!!
Lec 18: May 31st, 2006 EE512 - Graphical Models - J. Bilmes Page 5
• Other forms of inference.• Structure learning in graphical models
Summary of Last Time
Lec 18: May 31st, 2006 EE512 - Graphical Models - J. Bilmes Page 6
• When the inference gets hard …
Outline of Today’s Lecture
Lec 18: May 31st, 2006 EE512 - Graphical Models - J. Bilmes Page 7
Books and Sources for Today
• Various sources on approximate inference (see references in presentation below).
• Papers by Yedidia, 2000,2002,2004
Lec 18: May 31st, 2006 EE512 - Graphical Models - J. Bilmes Page 8
When exact inference is too expensive
1. Two general approaches: either an exact solution to an approximate problem, or an approximate solution to an exact problem.
2. Exact solution to approximate problem1. Structure learning: find a low tree-width (or “cheap” in some way)
graphical model that is still “high-quality” in some way, and then perform exact inference on the approximate model.
2. This can be easy or hard depending on the tree-width and on the measure of “high-quality”, and on the learning paradigm.
3. Approximate solution to an exact problem1. Approximate inference, tries to approximate in some way what
must be computed: Loopy Belief propagation, Variational/Mean-Field-Bethe/etc., and Sampling/Pruning, and hybrids between the above