CS Belief networks. Chapter

Transcription

1 CS Belief networks Chapter

2 CS Outline Conditional independence Bayesian networks: syntax and semantics Exact inference Approximate inference

3 CS Independence Two random variables ABare (absolutely) independent iff P (A B) =P (A) or P (A, B) =P (A B)P (B) =P (A)P (B) e.g., A and B are two coin tosses If n Boolean variables are independent, the full joint is P(X 1,...,X n )=Π i P(X i ) hence can be specified by just n numbers Absolute independence is a very strong requirement, seldom met

4 CS Conditional independence Consider the dentist problem with three random variables: T oothache, Cavity, Catch (steel probe catches in my tooth) The full joint distribution has = 7 independent entries If I have a cavity, the probability that the probe catches in it doesn t depend on whether I have a toothache: (1) P (Catch T oothache, Cavity) =P (Catch Cavity) i.e., Catch is conditionally independent of T oothache given Cavity The same independence holds if I haven t got a cavity: (2) P (Catch T oothache, Cavity)=P (Catch Cavity)

5 CS Conditional independence contd. Equivalent statements to (1) (1a) P (T oothache Catch,Cavity) =P (T oothache Cavity) Why?? (1b) P (T oothache, Catch Cavity)= P (T oothache Cavity)P (Catch Cavity) Why?? Full joint distribution can now be written as P(T oothache, Catch, Cavity) = P(T oothache, Catch Cavity)P(Cavity) = P(T oothache Cavity)P(Catch Cavity)P(Cavity) i.e., = 5 independent numbers (equations 1 and 2 remove 2)

6 CS Conditional independence contd. Equivalent statements to (1) (1a) P (T oothache Catch,Cavity) =P (T oothache Cavity) Why?? P (T oothache Catch,Cavity) = P (Catch T oothache, Cavity)P (T oothache Cavity)/P (Catch Cavity) = P (Catch Cavity)P (T oothache Cavity)/P (Catch Cavity) (from 1) = P (T oothache Cavity) (1b) P (T oothache, Catch Cavity)= P (T oothache Cavity)P (Catch Cavity) Why?? P (T oothache, Catch Cavity) = P (T oothache Catch,Cavity)P (Catch Cavity) (product rule) = P (T oothache Cavity)P (Catch Cavity) (from 1a)

7 CS Belief networks A simple, graphical notation for conditional independence assertions and hence for compact specification of full joint distributions Syntax: a set of nodes, one per variable a directed, acyclic graph (link directly influences ) a conditional distribution for each node given its parents: P(X i Parents(X i )) In the simplest case, conditional distribution represented as a conditional probability table (CPT)

8 CS Example I m at work, neighbor John calls to say my alarm is ringing, but neighbor Mary doesn t call. Sometimes it s set off by minor earthquakes. Is there a burglar? Variables: Burglar, Earthquake, Alarm, JohnCalls, MaryCalls Network topology reflects causal knowledge: Burglary P(B).001 Earthquake P(E).002 Alarm B T T F F E T F T F P(A) JohnCalls A T F P(J) MaryCalls A T F P(M) Note: k parents O(d k n) numbers vs. O(d n )

9 CS Semantics Global semantics defines the full joint distribution as the product of the local conditional distributions: P(X 1,...,X n )=Π n i =1P(X i Parents(X i )) e.g., P (J M A B E) is given by?? =

10 CS Semantics Global semantics defines the full joint distribution as the product of the local conditional distributions: P(X 1,...,X n )=Π n i =1P(X i Parents(X i )) e.g., P (J M A B E) is given by?? = P ( B)P ( E)P (A B E)P (J A)P (M A) Local semantics: each node is conditionally independent of its nondescendants given its parents Theorem: Local semantics global semantics

11 CS Markov blanket Each node is conditionally independent of all others given its Markov blanket: parents + children + children s parents U 1... U m Z 1j X Z nj Y 1... Y n

12 CS Constructing belief networks Need a method such that a series of locally testable assertions of conditional independence guarantees the required global semantics 1. Choose an ordering of variables X 1,...,X n 2. For i =1ton add X i to the network select parents from X 1,...,X i 1 such that P(X i Parents(X i )) = P(X i X 1,..., X i 1 ) This choice of parents guarantees the global semantics: P(X 1,...,X n )=Π n i =1P(X i X 1,..., X i 1 ) (chain rule) = Π n i =1P(X i Parents(X i )) by construction

13 CS Example Suppose we choose the ordering M, J, A, B, E MaryCalls JohnCalls P (J M) =P (J)?

14 CS Example Suppose we choose the ordering M, J, A, B, E MaryCalls JohnCalls Alarm P (J M) =P (J)? No P (A J, M)=P (A J)? P (A J, M)=P (A)?

15 CS Example Suppose we choose the ordering M, J, A, B, E MaryCalls JohnCalls Alarm Burglary P (J M) =P (J)? No P (A J, M)=P (A J)? P (A J, M)=P (A)? P (B A, J, M) =P (B A)? P (B A, J, M) =P (B)? No

16 CS Example Suppose we choose the ordering M, J, A, B, E MaryCalls JohnCalls Alarm Burglary Earthquake P (J M) =P (J)? No P (A J, M)=P (A J)? P (A J, M)=P (A)? 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)? No

17 CS Example Suppose we choose the ordering M, J, A, B, E MaryCalls JohnCalls Alarm Burglary Earthquake P (J M) =P (J)? No P (A J, M)=P (A J)? P (A J, M)=P (A)? 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 No

18 CS Example: Car diagnosis Initial evidence: engine won t start Testable variables (thin ovals), diagnosis variables (thick ovals) Hidden variables (shaded) ensure sparse structure, reduce parameters battery age alternator broken fanbelt broken battery dead no charging battery flat no oil no gas fuel line blocked starter broken lights oil light gas gauge engine won t start

19 CS Example: Car insurance Predict claim costs (medical, liability, property) given data on application form (other unshaded nodes) Age SeniorTrain GoodStudent RiskAversion SocioEcon Mileage VehicleYear ExtraCar DrivingSkill MakeModel DrivingHist Antilock DrivQuality Airbag CarValue HomeBase AntiTheft Ruggedness Accident OwnDamage Theft Cushioning OtherCost OwnCost MedicalCost LiabilityCost PropertyCost

20 CS Compact conditional distributions CPT grows exponentially with no. of parents CPT becomes infinite with continuous-valued parent or child Solution: canonical distributions that are defined compactly Deterministic nodes are the simplest case: X = f(parents(x)) for some function f E.g., Boolean functions N orthamerican Canadian U S M exican E.g., numerical relationships among continuous variables Level t = inflow + precipation - outflow - evaporation

21 CS Compact conditional distributions contd. Noisy-OR distributions model multiple noninteracting causes 1) Parents U 1...U k include all causes (can add leak node) 2) Independent failure probability q i for each cause alone P (X U 1...U j, U j+1... U k )=1 Π j i =1q i Cold F lu Malaria P (Fever) P ( Fever) F F F F F T F T F F T T = T F F T F T = T T F = T T T = Number of parameters linear in number of parents

22 CS Hybrid (discrete+continuous) networks Discrete (Subsidy? and Buys?); continuous (Harvest and Cost) Subsidy? Harvest Cost Buys? Option 1: discretization possibly large errors, large CPTs Option 2: finitely parameterized canonical families 1) Continuous variable, discrete+continuous parents (e.g., Cost) 2) Discrete variable, continuous parents (e.g., Buys?)

23 CS Continuous child variables Need one conditional density function for child variable given continuous parents, for each possible assignment to discrete parents Most common is the linear Gaussian model, e.g.,: P (Cost= c Harvest= h, Subsidy?=true) = N(a t h + b t,σ t )(c) ( 1 = exp 1 ( ) ) 2 c (at h + b t ) σ t 2π 2 σ t Mean Cost varies linearly with Harvest,varianceisfixed Linear variation is unreasonable over the full range but works OK if the likely range of Harvest is narrow

24 CS Continuous child variables P(Cost Harvest,Subsidy?=true) P(Cost Harvest,Subsidy?=false) P(Cost Harvest) Cost Harvest 0 5 Cost Harvest 0 5 Cost Harvest All-continuous network with LG distributions full joint is a multivariate Gaussian Discrete+continuous LG network is a conditional Gaussian network i.e., a multivariate Gaussian over all continuous variables for each combination of discrete variable values

25 CS Discrete variable w/ continuous parents Probability of Buys? given Cost should be a soft threshold: P(Buys?=false Cost=c) Cost c Probit distribution uses integral of Gaussian: Φ(x) = x N(0, 1)(x)dx P (Buys?=true Cost= c) =Φ(( c + µ)/σ)

26 CS Can view as hard threshold whose location is subject to noise

27 CS Discrete variable contd. Sigmoid (or logit) distribution also used in neural networks: 1 P (Buys?=true Cost= c) = 1+exp( 2 c+µ σ ) Sigmoid has similar shape to probit but much longer tails: P(Buys?=false Cost=c) Cost c