# Bayesian Networks Chapter 14. Mausam (Slides by UW-AI faculty & David Page)

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1 Bayesian Networks Chapter 14 Mausam (Slides by UW-AI faculty & David Page)

2 Bayes Nets In general, joint distribution P over set of variables (X 1 x... x X n ) requires exponential space for representation & inference BNs provide a graphical representation of conditional independence relations in P usually quite compact requires assessment of fewer parameters, those being quite natural (e.g., causal) efficient (usually) inference: query answering and belief update D. Weld and D. Fox 2

3 Back at the dentist s Topology of network encodes conditional independence assertions: Weather is independent of the other variables Toothache and Catch are conditionally independent of each other given Cavity D. Weld and D. Fox 3

4 Syntax a set of nodes, one per random variable a directed, acyclic graph (link "directly influences") a conditional distribution for each node given its parents: P (X i Parents (X i )) For discrete variables, conditional probability table (CPT)= distribution over X i for each combination of parent values D. Weld and D. Fox 4

5 Burglars and Earthquakes You are at a Done with the AI class party. Neighbor John calls to say your home alarm has gone off (but neighbor Mary doesn't). Sometimes your alarm is set off by minor earthquakes. Question: Is your home being burglarized? Variables: Burglary, Earthquake, Alarm, JohnCalls, MaryCalls Network topology reflects "causal" knowledge: A burglar can set the alarm off An earthquake can set the alarm off The alarm can cause Mary to call The alarm can cause John to call D. Weld and D. Fox 5

6 Burglars and Earthquakes Pr(E=t) Pr(E=f) Earthquake Burglary Pr(B=t) Pr(B=f) Pr(JC A) a 0.9 (0.1) a 0.05 (0.95) Alarm Pr(A E,B) e,b 0.95 (0.05) e,b 0.29 (0.71) e,b 0.94 (0.06) e,b (0.999) JohnCalls MaryCalls Pr(MC A) a 0.7 (0.3) a 0.01 (0.99) D. Weld and D. Fox 6

7 Earthquake Example Earthquake Burglary (cont d) Alarm JohnCalls MaryCalls If we know Alarm, no other evidence influences our degree of belief in JohnCalls P(JC MC,A,E,B) = P(JC A) also: P(MC JC,A,E,B) = P(MC A) and P(E B) = P(E) By the chain rule we have P(JC,MC,A,E,B) = P(JC MC,A,E,B) P(MC A,E,B) P(A E,B) P(E B) P(B) = P(JC A) P(MC A) P(A B,E) P(E) P(B) Full joint requires only 10 parameters (cf. 32) D. Weld and D. Fox 7

8 Earthquake Example (Global Semantics) Earthquake JohnCalls Alarm Burglary MaryCalls We just proved P(JC,MC,A,E,B) = P(JC A) P(MC A) P(A B,E) P(E) P(B) In general full joint distribution of a Bayes net is defined as P( X 1, X 2,..., Xn) P( Xi Par ( Xi)) n i 1 D. Weld and D. Fox 8

9 BNs: Qualitative Structure Graphical structure of BN reflects conditional independence among variables Each variable X is a node in the DAG Edges denote direct probabilistic influence usually interpreted causally parents of X are denoted Par(X) Local semantics: X is conditionally independent of all nondescendents given its parents Graphical test exists for more general independence Markov Blanket D. Weld and D. Fox 9

10 Given Parents, X is Independent of Non-Descendants D. Weld and D. Fox 10

11 Examples Earthquake Burglary Alarm JohnCalls MaryCalls D. Weld and D. Fox 11

12 For Example Earthquake Burglary Alarm JohnCalls MaryCalls D. Weld and D. Fox 12

13 For Example Earthquake Burglary Radio Alarm JohnCalls MaryCalls D. Weld and D. Fox 13

14 For Example Earthquake Burglary Radio Alarm JohnCalls MaryCalls D. Weld and D. Fox 14

15 For Example Earthquake Burglary Radio Alarm JohnCalls MaryCalls D. Weld and D. Fox 15

16 Given Markov Blanket, X is Independent of All Other Nodes MB(X) = Par(X) Childs(X) Par(Childs(X)) D. Weld and D. Fox 16

17 For Example Earthquake Burglary Radio Alarm JohnCalls MaryCalls D. Weld and D. Fox 17

18 For Example Earthquake Burglary Radio Alarm JohnCalls MaryCalls D. Weld and D. Fox 18

19 d-separation An undirected path between two nodes is cut off if information cannot flow across one of the nodes in the path Two nodes are d-separated if every undirected path between them is cut off Two sets of nodes are d-separated if every pair of nodes, one from each set, is d-separated

20 d-separation A B C Linear connection: Information can flow between A and C if and only if we do not have evidence at B

21 For Example Earthquake Burglary Alarm JohnCalls MaryCalls D. Weld and D. Fox 21

22 d-separation (continued) A B C Diverging connection: Information can flow between A and C if and only if we do not have evidence at B

23 For Example Earthquake Burglary Alarm JohnCalls MaryCalls D. Weld and D. Fox 23

24 d-separation (continued) A B C D E Converging connection: Information can flow between A and C if and only if we do have evidence at B or any descendent of B (such as D or E)

25 For Example Earthquake Burglary Alarm JohnCalls MaryCalls D. Weld and D. Fox 25

26 Note: For Some CPT Choices, More Conditional Independences May Hold Suppose we have: A B C Then only conditional independence we have is: P(A C B) Now choose CPTs such that A must be True, B must take same value as A, and C must take same value as B In the resulting distribution P, all pairs of variables are conditionally independent given the third

27 Bayes Net Construction Example Suppose we choose the ordering M, J, A, B, E P(J M) = P(J)? D. Weld and D. Fox 28

28 Example Suppose we choose the ordering M, J, A, B, E P(J M) = P(J)? No P(A J, M) = P(A J)? P(A M)? P(A)? D. Weld and D. Fox 29

29 Example Suppose we choose the ordering M, J, A, B, E P(J M) = P(J)? No P(A J, M) = P(A J)? P(A J, M) = P(A)? No P(B A, J, M) = P(B A)? P(B A, J, M) = P(B)? D. Weld and D. Fox 30

30 Example Suppose we choose the ordering M, J, A, B, E P(J M) = P(J)? No P(A J, M) = P(A J)? P(A J, M) = P(A)? No P(B A, J, M) = P(B A)? Yes P(B A, J, M) = P(B)? No P(E B, A,J, M) = P(E A)? P(E B, A, J, M) = P(E A, B)? D. Weld and D. Fox 31

31 Example Suppose we choose the ordering M, J, A, B, E P(J M) = P(J)? No P(A J, M) = P(A J)? P(A J, M) = P(A)? No P(B A, J, M) = P(B A)? Yes P(B A, J, M) = P(B)? No P(E B, A,J, M) = P(E A)? No P(E B, A, J, M) = P(E A, B)? Yes D. Weld and D. Fox 32

32 Example contd. Deciding conditional independence is hard in noncausal directions (Causal models and conditional independence seem hardwired for humans!) Network is less compact: = 13 numbers needed D. Weld and D. Fox 33

33 Example: Car Diagnosis D. Weld and D. Fox 34

34 Example: Car Insurance D. Weld and D. Fox 35

35 Medical Diagnosis Other Applications Computational Biology and Bioinformatics Natural Language Processing Document classification Image processing Decision support systems Ecology & natural resource management Robotics Forensic science D. Weld and D. Fox 36

36 Compact Conditionals D. Weld and D. Fox 37

37 Compact Conditionals D. Weld and D. Fox 38

38 Hybrid (discrete+cont) Networks D. Weld and D. Fox 39

39 #1: Continuous Child Variables D. Weld and D. Fox 40

40 #2 Discrete child cont. parents D. Weld and D. Fox 41

41 Why probit? D. Weld and D. Fox 42

42 Sigmoid Function D. Weld and D. Fox 43

43 Inference in BNs The graphical independence representation yields efficient inference schemes We generally want to compute Marginal probability: Pr(Z), Pr(Z E) where E is (conjunctive) evidence Z: query variable(s), E: evidence variable(s) everything else: hidden variable Computations organized by network topology D. Weld and D. Fox 44

44 P(B J=true, M=true) Earthquake Burglary Alarm JohnCalls MaryCalls P(b j,m) = P(b,j,m,e,a) e,a D. Weld and D. Fox 45

45 P(B J=true, M=true) Earthquake Burglary Alarm JohnCalls MaryCalls P(b j,m) = P(b) P(e) P(a b,e)p(j a)p(m a) e a D. Weld and D. Fox 46

46 Variable Elimination P(b j,m) = P(b) P(e) P(a b,e)p(j a)p(m,a) e a Repeated computations Dynamic Programming D. Weld and D. Fox 47

47 Variable Elimination A factor is a function from some set of variables into a specific value: e.g., f(e,a,n1) CPTs are factors, e.g., P(A E,B) function of A,E,B VE works by eliminating all variables in turn until there is a factor with only query variable To eliminate a variable: join all factors containing that variable (like DB) sum out the influence of the variable on new factor exploits product form of joint distribution D. Weld and D. Fox 48

48 Example of VE: P(JC) P(J) = M,A,B,E P(J,M,A,B,E) = M,A,B,E P(J A)P(M A) P(B)P(A B,E)P(E) = A P(J A) M P(M A) B P(B) E P(A B,E)P(E) = A P(J A) M P(M A) B P(B) f1(a,b) = A P(J A) M P(M A) f2(a) = A P(J A) f3(a) = f4(j) Earthqk JC Alarm Burgl MC D. Weld and D. Fox 49

49 Notes on VE Each operation is a simple multiplication of factors and summing out a variable Complexity determined by size of largest factor in our example, 3 vars (not 5) linear in number of vars, exponential in largest factor elimination ordering greatly impacts factor size optimal elimination orderings: NP-hard heuristics, special structure (e.g., polytrees) Practically, inference is much more tractable using structure of this sort D. Weld and D. Fox 50

50 Earthquake Burglary Irrelevant variables Alarm P(J) = M,A,B,E P(J,M,A,B,E) = M,A,B,E P(J A)P(B)P(A B,E)P(E)P(M A) = A P(J A) B P(B) E P(A B,E)P(E) M P(M A) = A P(J A) B P(B) E P(A B,E)P(E) = A P(J A) B P(B) f1(a,b) = A P(J A) f2(a) JohnCalls MaryCalls = f3(j) M is irrelevant to the computation Thm: Y is irrelevant D. Weld and D. unless Fox Y ϵ Ancestors(Z U 51 E)

51 Reducing 3-SAT to Bayes Nets D. Weld and D. Fox 52

52 Complexity of Exact Inference Exact inference is NP hard 3-SAT to Bayes Net Inference It can count no. of assignments for 3-SAT: #P complete Inference in tree-structured Bayesian network Polynomial time compare with inference in CSPs Approximate Inference Sampling based techniques D. Weld and D. Fox 53

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