1. BIOL 301 Guest Lecture: Reasoning Under Uncertainty (Intro to Bayes Networks) Simon D. Levy CSCI Department 8 April 2010

2. Review of Bayes’ Rule From the Product Rule: P(A|B) = P(A & B) / P(B) P(A &B) = P(A|B) * P(B) = P(B|A) * P(A) Rev. Thomas Bayes (1702-1761) We derive Bayes’ Rule by substitution: P(A|B) = P(A & B) / P(B) = P(B|A) * P(A) / P(B)

3. Real-World Problems May Involve Many Variables http://www.bayesia.com/assets/images/content/produits/blab/tutoriel/en/chapitre-3/image026.jpg

4. Real-World Problems May Involve Many Variables

5. Variables Are Typically Oberserved Simultaneously (Confounded) FeverAcheVirus P NoNoNo.950 NoNoYes.002 NoYesNo.032 NoYesYes.002 YesNoNo.002 YesNoYes.001 YesYesNo.010 YesYesYes.001 So how do we compute P(V=Yes), P(F=Yes & A=No), etc.?

6. Marginalization FeverAcheVirus P NoNoNo.950 NoNoYes.002 NoYesNo.032 NoYesYes.002 YesNoNo.002 YesNoYes.001 YesYesNo.010 YesYesYes.001 ______ Sum =.006 P(V=Yes):

7. Marginalization FeverAcheVirus P NoNoNo.950 NoNoYes.002 NoYesNo.032 NoYesYes.002 YesNoNo.002 YesNoYes.001 YesYesNo.010 YesYesYes.001 ______ Sum =.003 P(F=Yes & A=No):

8. Combinatorial Explosion (The “Curse of Dimensionality”) Assuming (unrealistically) only two values (Yes/No) per variable: # Variables# of Rows in Table 12 24 38 416 532 664: 20 1,048,576

9. Solution: Local Causality + Belief Propagation

10. Local Causality

11. Recover Joint From Prior & Posterior BEP(A) TT.95 TF.94 FT.29 FF.001 P(B).001 P(E).002 B E A Prob T T T.001*.002*.95 =.000001900 T T F.001*.002*.05 =.000000100 T F T.001*.998*.94 =.000938120 T F F.001*.998*.06 =.000059880 F T T.999*.002*.29 =.000579420 F T F.999*.002*.71 =.001418580 F F T.999*.998*.001 =.000997002 F F F.999*.998*.999 =.996004998

12. Belief Propagation Consider just B → A → J P(J=T | B=T) = P(J=T | A=T) * P(A=T | B=T) Then use Bayes’ Rule and marginalization to answer more sophisticated queries like P(B=T | J=F & E=F & M=T)

13. Multiply-Connected Networks Wet Grass Cloudy Rain Sprinkler

14. Clustering (“Mega Nodes”) Cloudy Sprinkler Rain Sprinkler Rain Wet Grass Sprinkler Rain

15. A B D F E C G H Junction Tree Algorithm (Huang & Darwiche 1994)

16. A B D F E C G H A B D F E C G H “Moralize””

17. A B D F E C G H Junction Tree Algorithm (Huang & Darwiche 1994)

18. A B D F E C G H A B D F E C G H Triangulate

19. Junction Tree Algorithm (Huang & Darwiche 1994) A B D F E C G H

20. A B D F E C G H ABDADE DEF ACECEG EGH AD DE AECE EG

21. “Message-Passing” ABDADE DEF ACECEG EGH AD DE AECE EG Observe A=T

22. “Message-Passing” ABDADE DEF ACECEG EGH AD DE AECE EG Pick a cluster containing A:

23. “Message-Passing” ABDADE DEF ACECEG EGH AD DE AECE EG Pass messages to propagate evidence: