Inference in Bayesian networks

Transcription

1 Inference in Bayesian networks AIMA2e hapter Outline Exact inference by enumeration Exact inference by variable elimination Approximate inference by stochastic simulation Approximate inference by Markov chain Monte arlo 2

2 Inference tasks imple queries: compute posterior marginal P(X i E = e) e.g., P (NoGas Gauge = empty, Lights = on, tarts = false) onjunctive queries: P(X i, X j E = e) =P(X i E = e)p(x j X i, E = e) Optimal decisions: decision networks include utility information; probabilistic inference required for P (outcome action, evidence) Value of information: which evidence to seek next? ensitivity analysis: which probability values are most critical? Explanation: why do I need a new starter motor? 3 Inference by enumeration lightly intelligent way to sum out variables from the joint without actually constructing its explicit representation imple query on the burglary network: B P(B j, m) = P(B, j, m)/p(j, m) A = αp(b, j, m) = α e ap(b, e, a, j, m) J E M Rewrite full joint entries using product of P entries: P(B j, m) = α e ap(b)p(e)p(a B, e)p(j a)p(m a) = αp(b) e P(e) ap(a B, e)p(j a)p(m a) Recursive depth-first enumeration: O(n) space, O(d n ) time 4

3 Enumeration algorithm function ENUMERAION-AK(X, e, bn) returns a distribution over X inputs: X, the query variable e, observed values for variables E bn, a Bayesian network with variables {X} E Y Q(X ) a distribution over X, initially empty for each value x i of X do extend e with value x i for X Q(x i ) ENUMERAE-ALL(VAR[bn], e) return NORMALIZE(Q(X)) function ENUMERAE-ALL(vars, e) returns a real number if EMPY?(vars) then return 1.0 Y IR(vars) if Y has value y in e then return P (y Pa(Y )) ENUMERAE-ALL(RE(vars), e) else return P (y Pa(Y )) ENUMERAE-ALL(RE(vars), e y) y where e y is e extended with Y = y 5 Evaluation tree Enumeration is inefficient: repeated computation e.g., computes P (j a)p (m a) for each value of e P(b).001 P(e).002 P( e).998 P(a b,e) P( a b,e) P(a b, e) P( a b, e) P(j a) P(j a) P(j a).05 P(j a).05 P(m a) P(m a) P(m a) P(m a).70.01

4 Inference by variable elimination Variable elimination: carry out summations right-to-left, storing intermediate results (factors) to avoid recomputation P(B j, m) = α P(B) }{{} B e P (e) }{{} E a P(a B, e) P (j a) P (m a) }{{}}{{}}{{} A J M = αp(b) e P(e) a P(a B, e)p(j a)f M(a) = αp(b) e P(e) a P(a B, e)f J(a)f M (a) = αp(b) e P(e) a f A(a, b, e)f J (a)f M (a) = αp(b) ep(e)fājm(b, e) (sum out A) = αp(b)f ĒĀJM (b) (sum out E) = αf B (b) f (b) ĒĀJM 7 Variable elimination: Basic operations umming out a variable from a product of factors: move any constant factors outside the summation add up submatrices in pointwise product of remaining factors x f 1 f k = f 1 f i x f i+1 f k = f 1 f i f X assuming f 1,...,f i do not depend on X Pointwise product of factors f 1 and f 2 : f 1 (x 1,...,x j,y 1,...,y k ) f 2 (y 1,...,y k,z 1,...,z l ) = f(x 1,...,x j,y 1,...,y k,z 1,...,z l ) E.g., f 1 (a, b) f 2 (b, c) =f(a, b, c) 8

5 Variable elimination algorithm function ELIMINAION-AK(X, e, bn) returns a distribution over X inputs: X, the query variable e, evidence specified as an event bn, a belief network specifying joint distribution P(X 1,...,X n ) factors []; vars REVERE(VAR[bn]) for each var in vars do factors [MAKE-AOR(var, e) factors] if var is a hidden variable then factors UM-OU(var, factors) return NORMALIZE(POINWIE-PRODU(factors)) 9 Irrelevant variables onsider the query P (Johnalls Burglary = true) P (J b) =αp (b) P (e) P (a b, e)p (J a) P (m a) e a m B A E um over m is identically 1; M is irrelevant to the query J M hm 1: Y is irrelevant unless Y Ancestors({X} E) Here, X = Johnalls, E = {Burglary},and Ancestors({X} E) ={Alarm, Earthquake} so M is irrelevant (ompare this to backward chaining from the query in Horn clause KBs) 10

6 L L L L Irrelevant variables contd. Defn: moral graph of Bayes net: marry all parents and drop arrows Defn: A is m-separated from B by iff separated by in the moral graph hm 2: Y is irrelevant if m-separated from X by E or P (Johnalls Alarm = true), both Burglary and Earthquake are irrelevant B A E J M 11 omplexity of exact inference ingly connected networks (or polytrees): any two nodes are connected by at most one (undirected) path time and space cost of variable elimination are O(d k n) Multiply connected networks: can reduce 3A to exact inference NP-hard equivalent to counting 3A models #P-complete A B D 1. A v B v 2. v D v A B v v D AND 12

7 Inference by stochastic simulation Basic idea: 1) Draw N samples from a sampling distribution 2) ompute an approximate posterior probability ˆP 3) how this converges to the true probability P 0.5 oin Outline: ampling from an empty network Rejection sampling: reject samples disagreeing with evidence Likelihood weighting: use evidence to weight samples Markov chain Monte arlo (MM): sample from a stochastic process whose stationary distribution is the true posterior 13 ampling from an empty network function PRIOR-AMPLE(bn) returns an event sampled from bn inputs: bn, a belief network specifying joint distribution P(X 1,...,X n ) x an event with n elements for i = 1to n do x i a random sample from P(X i Parents(X i )) return x 14

8 Example loudy P(R ) P( ) R.99 P(W,R) P() Example loudy P(R ) P( ) R.99 P(W,R) P().01 16

9 Example loudy P(R ) P( ) R.99 P(W,R) P() Example loudy P(R ) P( ) R.99 P(W,R) P().01 18

10 Example loudy P(R ) P( ) R.99 P(W,R) P() Example loudy P(R ) P( ) R.99 P(W,R) P().01 20

11 Example P() loudy P( ) P(R ) R P(W,R) ampling from an empty network contd. Probability that PRIORAMPLE generates a particular event P (x 1...x n )= n i =1 P (x i Parents(X i )) = P (x 1...x n ) i.e., the true prior probability E.g., P (t, f, t, t) = =0.324 = P (t, f, t, t) Let N P (x 1...x n ) be the number of samples generated for event x 1,...,x n hen we have lim N ˆP (x 1,...,x n ) = lim P(x 1,...,x n )/N N = P (x 1,...,x n ) = P (x 1...x n ) hat is, estimates derived from PRIORAMPLE are consistent horthand: ˆP (x 1,...,x n ) P (x 1...x n ) 22

12 Rejection sampling ˆP(X e) estimated from samples agreeing with e function REJEION-AMPLING(X, e, bn, N) returns an estimate of P (X e) local variables: N, a vector of counts over X, initially zero for j =1toN do x PRIOR-AMPLE(bn) if x is consistent with ethen N[x] N[x]+1 where x is the value of X in x return NORMALIZE(N[X]) E.g., estimate P( = true) using 100 samples 27 samples have = true Of these, 8 have = true and 19 have = false. ˆP( = true) =NORMALIZE(< 8, 19 >) =< 0.296, > imilar to a basic real-world empirical estimation procedure 23 Analysis of rejection sampling ˆP(X e) =αn P (X, e) (algorithm defn.) = N P (X, e)/n P (e) (normalized by N P (e)) P(X, e)/p(e) (property of PRIORAMPLE) = P(X e) (defn. of conditional probability) Hence rejection sampling returns consistent posterior estimates Problem: hopelessly expensive if P (e) is small P (e) drops off exponentially with number of evidence variables! 24

13 Likelihood weighting Idea: fix evidence variables, sample only nonevidence variables, and weight each sample by the likelihood it accords the evidence function LIKELIHOOD-WEIGHING(X, e, bn, N) returns an estimate of P (X e) local variables: W, a vector of weighted counts over X, initially zero for j =1toN do x, w WEIGHED-AMPLE(bn) W[x ] W[x ]+w where x is the value of X in x return NORMALIZE(W[X ]) function WEIGHED-AMPLE(bn, e) returns an event and a weight x an event with n elements; w 1 for i =1to n do if X i has a value x i in e then w w P (X i = x i Parents(X i )) else x i a random sample from P(X i Parents(X i )) return x, w 25 Likelihood weighting example P() loudy P( ) P(R ) R P(W,R) w =1.0 26

14 Likelihood weighting example loudy P(R ) P( ) R.99 P(W,R) P().01 w = Likelihood weighting example loudy P(R ) P( ) R.99 P(W,R) P().01 w =1.0 28

15 Likelihood weighting example P() loudy P( ) P(R ) R P(W,R) w = Likelihood weighting example P() loudy P( ) P(R ) R P(W,R) w =

16 Likelihood weighting example P() loudy P( ) P(R ) R P(W,R) w = Likelihood weighting example P() loudy P( ) P(R ) R P(W,R) w = =

17 Likelihood weighting analysis ampling probability for WEIGHEDAMPLE is W (z, e) = l i =1 P (z i Parents(Z i )) Note: pays attention to evidence in ancestors only somewhere in between prior and posterior distribution Weight for a given sample z, e is w(z, e) = m i =1 P (e i Parents(E i )) Hence likelihood weighting returns consistent estimates but performance still degrades with many evidence variables because a few samples have nearly all the total weight loudy Weighted sampling probability is W (z, e)w(z, e) = l i =1 P (z i Parents(Z i )) m i =1 P (e i Parents(E i )) = P (z, e) (by standard global semantics of network) 33 Approximate inference using MM tate of network = current assignment to all variables. Generate next state by sampling one variable given Markov blanket ample each variable in turn, keeping evidence fixed function MM-AK(X, e, bn, N) returns an estimate of P (X e) local variables: N[X ], a vector of counts over X, initially zero Z, the nonevidence variables in bn x, the current state of the network, initially copied from e initialize x with random values for the variables in Y for j =1toN do N[x ] N[x ]+1where x is the value of X in x for each Z i in Zdo sample the value of Z i in x from P(Z i MB(Z i )) given the values of MB(Z i ) in x return NORMALIZE(N[X ]) an also choose a variable to sample at random each time 34

18 he Markov chain With = true, W et = true, there are four states: loudy loudy loudy loudy Wander about for a while, average what you see 35 MM example contd. Estimate P( = true, = true) ample loudy or given its Markov blanket, repeat. ount number of times is true and false in the samples. E.g., visit 100 states 31 have = true, 69have = false ˆP( = true, W et = true) = NORMALIZE(< 31, 69 >) =< 0.31, 0.69 > heorem: chain approaches stationary distribution: long-run fraction of time spent in each state is exactly proportional to its posterior probability 36

19 Markov blanket sampling Markov blanket of loudy is and Markov blanket of is loudy,,and loudy Probability given the Markov blanket is calculated as follows: P (x i MB(X i)) = P (x i Parents(X i)) Z j hildren(x i ) P (z j Parents(Z j )) Easily implemented in message-passing parallel systems, brains Main computational problems: 1) Difficult to tell if convergence has been achieved 2) an be wasteful if Markov blanket is large: P (X i MB(X i )) won t change much (law of large numbers) 37 ummary Exact inference by variable elimination: polytime on polytrees, NP-hard on general graphs space = time, very sensitive to topology Approximate inference by LW, MM: LW does poorly when there is lots of (downstream) evidence LW, MM generally insensitive to topology onvergence can be very slow with probabilities close to 1 or 0 an handle arbitrary combinations of discrete and continuous variables 38