1. Belief Propagation Revisited Adnan Darwiche

2. Graphical Models Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start

3. Graphical Models Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start ON OFF OK WEAK DEAD Lights Battery Power.99.01.20.80 01

4. Graphical Models Battery Age Alternator Fan Belt Battery Charge Delivered Battery Power Starter Radio LightsEngine Turn Over Gas Gauge Gas Fuel Pump Fuel Line Distributor Spark Plugs Engine Start

5. Probabilistic Reasoning in… Diagnosis Planning Learning Channel coding Vision Speech recognition Language comprehension Bioinformatics …

6. Treewidth w

7. Larger Shift  Closer Object Smaller Shift  Further Object

8. Input: L&R Image Output: Depth Map Bayesian Network Images Define a Bayesian Network Reasoning in Bayesian Network Estimates Depth

9. Belief Propagation

11. Belief Propagation: What if there are loops? ? ?

13. Loopy Belief Propagation p.235

14. The Merit of Loopy Belief Propagation Revolutionary error correcting codes: Turbo Codes (Berrou&Glavieux 1993) LDPC Codes (MacKay&Neal 1995, Gallager 1962) Can closely reach the theoretical limit of communications in noisy channels Turbo & LDPC decoders: Loopy BP in BNs! (McEliece, MacKay & Cheng 1998)

15. Stereo Vision Two ImagesDepth Map

16. Stereo Vision http://vision.middlebury.edu/stereo/eval/ Top 4 highest ranking are loopy BP or extend loopy BP

17. Edge Deletion Semantics (Joint work with Arthur Choi) Energy-Based Semantics (Statistical Physics)

18. The Idea A C B D

19. A C B D A C B D

20. A C B D A C B D X Y New Edge Parameters for each Query

21. Specifying the Approximation How do we parametrize edges? Quality of approximation Which edges do we delete? Quality of approximation Computational complexity

22. Parametrizing Edges: ED-BP U X U' s' Choose parameters that satisfy:

23. Parametrizing Edges Iteratively: ED-BP Iteration t = 0 Initialization

24. Parametrizing Edges Iteratively: ED-BP Iteration t = 1

25. Parametrizing Edges Iteratively: ED-BP Iteration t = 2

26. Parametrizing Edges Iteratively: ED-BP Iteration t c Convergence

27. Belief Propagation as Edge Deletion Iteration t

31. Which Edges To Delete? X U' s' U

32. Which Edges To Delete? X U' s' U

33. ED-BP: Improving on the Quality of IBP Exact Inference BP

34. ED-BP: Improving on the Quality of IBP Exact Inference BP

35. ED-BP: Potentially Bad Approximations Exact Inference BP Unimproved, but costly, approximation,

36. ED-BP: Improving on the Convergence Rate

37. ED-BP: Improving on Running Time

38. Edge Deletion in Undirected Models Original Network Approximate Network

39. Correcting the Partition Function I i j Theorem: If MI(X i,X j ) = 0 in ED-BP network M', then: where

40. Deleting Many Edges This will yield the Bethe free energy approximation!

41. Correcting the Partition Function II i j Theorem: For an ED-BP network M', we have: where

42. Which Edges Do We Recover? EC2? ij Recover edges with largest  kl MI(X i,X j ;X k X l ) !

43. Experiment: Random Grid 8079640 5 10 15 x 10 -3 edges recovered |log Z - log Z'| EC1,rand EC2,rand EC1,MI EC2,MI EC1,2MI EC2,2MI Bethe exact yfq

44. Beyond Treewidth… Exact Inference: Exploit Non-structural Independence Approximate inference: Exact inference on an approximate network obtained by deleting edges

45. What next? Constant factors! Guarantees/bounds on approximations Edge recovery heuristics: getting the most out of the extra time Controlling tradeoff between quality & complexity Dynamic models Logical reasoning: Survey propagation

46. http://reasoning.cs.ucla.edu