Inference in Bayesian networks


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1 Inference in Bayesian networks hapter hapter
2 Outline Exact inference by enumeration Exact inference by variable elimination Approximate inference by stochastic simulation Approximate inference by Markov chain Monte arlo hapter
3 Inference tasks Simple queries: compute posterior marginal P(X i E = e) e.g., P (NoGas Gauge = empty, Lights = on, Starts = 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? Sensitivity analysis: which probability values are most critical? Explanation: why do I need a new starter motor? hapter
4 Inference by enumeration Slightly intelligent way to sum out variables from the joint without actually constructing its explicit representation Simple query on the burglary network: P(B j, m) = P(B, j, m)/p (j, m) = αp(b, j, m) = α Σ e Σ a P(B, e, a, j, m) B J A E M Rewrite full joint entries using product of P entries: P(B j, m) = α Σ e Σ a P(B)P (e)p(a B, e)p (j a)p (m a) = αp(b) Σ e P (e) Σ a P(a B, e)p (j a)p (m a) Recursive depthfirst enumeration: O(n) space, O(d n ) time hapter
5 Enumeration algorithm function EnumerationAsk(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 ) EnumerateAll(Vars[bn], e) return Normalize(Q(X )) function EnumerateAll(vars, e) returns a real number if Empty?(vars) then return 1.0 Y irst(vars) if Y has value y in e then return P (y P a(y )) EnumerateAll(Rest(vars), e) else return y P (y P a(y )) EnumerateAll(Rest(vars), e y ) where e y is e extended with Y = y hapter
6 Evaluation tree P(b).001 P(e).002 P( e).998 P(a b,e).95 P( a b,e).05 P(a b, e).94 P( a b, e).06 P(j a) P(j a).05 P(j a) P(j a).05 P(m a) P(m a) P(m a) P(m a) Enumeration is inefficient: repeated computation e.g., computes P (j a)p (m a) for each value of e hapter
7 Inference by variable elimination Variable elimination: carry out summations righttoleft, storing intermediate results (factors) to avoid recomputation P(B j, m) = α P(B) } {{ } B Σ e P (e) }{{} E Σ a P(a B, e) }{{} A P (j a) } {{ } J = αp(b)σ e P (e)σ a P(a B, e)p (j a)f M (a) = αp(b)σ e P (e)σ a P(a B, e)f J (a)f M (a) = αp(b)σ e P (e)σ a f A (a, b, e)f J (a)f M (a) = αp(b)σ e P (e)f ĀJM (b, e) (sum out A) = αp(b)f ĒĀJM (b) (sum out E) = αf B (b) f ĒĀJM (b) P (m a) } {{ } M hapter
8 Variable elimination: Basic operations Summing out a variable from a product of factors: move any constant factors outside the summation add up submatrices in pointwise product of remaining factors Σ x f 1 f k = f 1 f i Σ 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) hapter
9 Variable elimination algorithm function EliminationAsk(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 Reverse(Vars[bn]) for each var in vars do factors [Makeactor(var, e) factors] if var is a hidden variable then factors SumOut(var, factors) return Normalize(PointwiseProduct(factors)) hapter
10 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 Sum over m is identically 1; M is irrelevant to the query B J A E M hm 1: Y is irrelevant unless Y Ancestors({X} E) Here, X = Johnalls, E = {Burglary}, and Ancestors({X} E) = {Alarm, Earthquake} so M aryalls is irrelevant (ompare this to backward chaining from the query in Horn clause KBs) hapter
11 Irrelevant variables contd. Defn: moral graph of Bayes net: marry all parents and drop arrows Defn: A is mseparated from B by iff separated by in the moral graph hm 2: Y is irrelevant if mseparated from X by E B E or P (Johnalls Alarm = true), both Burglary and Earthquake are irrelevant J A M hapter
12 L L L L omplexity of exact inference Singly 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 3SA to exact inference NPhard equivalent to counting 3SA models #Pcomplete A B D 1. A v B v 2. v D v A B v v D AND hapter
13 Inference by stochastic simulation Basic idea: 1) Draw N samples from a sampling distribution S 2) ompute an approximate posterior probability ˆP 3) Show this converges to the true probability P 0.5 oin Outline: Sampling 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 hapter
14 Sampling from an empty network function PriorSample(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 = 1 to n do x i a random sample from P(X i parents(x i )) given the values of P arents(x i ) in x return x hapter
15 Example P() loudy P(S ).10 P(R ) S R P(W S,R) hapter
16 Example P() loudy P(S ).10 P(R ) S R P(W S,R) hapter
17 Example P() loudy P(S ).10 P(R ) S R P(W S,R) hapter
18 Example P() loudy P(S ).10 P(R ) S R P(W S,R) hapter
19 Example P() loudy P(S ).10 P(R ) S R P(W S,R) hapter
20 Example P() loudy P(S ).10 P(R ) S R P(W S,R) hapter
21 Example P() loudy P(S ).10 P(R ) S R P(W S,R) hapter
22 Sampling from an empty network contd. Probability that PriorSample generates a particular event S P S (x 1... x n ) = Π n i = 1P (x i parents(x i )) = P (x 1... x n ) i.e., the true prior probability E.g., S P S (t, f, t, t) = = = P (t, f, t, t) Let N P S (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 N P S(x 1,..., x n )/N N = S P S (x 1,..., x n ) = P (x 1... x n ) hat is, estimates derived from PriorSample are consistent Shorthand: ˆP (x1,..., x n ) P (x 1... x n ) hapter
23 Rejection sampling ˆP(X e) estimated from samples agreeing with e function RejectionSampling(X, e, bn, N) returns an estimate of P (X e) local variables: N, a vector of counts over X, initially zero for j = 1 to N do x PriorSample(bn) if x is consistent with e then 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, Similar to a basic realworld empirical estimation procedure hapter
24 Analysis of rejection sampling ˆP(X e) = αn P S (X, e) (algorithm defn.) = N P S (X, e)/n P S (e) (normalized by N P S (e)) P(X, e)/p (e) (property of PriorSample) = 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! hapter
25 Likelihood weighting Idea: fix evidence variables, sample only nonevidence variables, and weight each sample by the likelihood it accords the evidence function LikelihoodWeighting(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 = 1 to N do x, w WeightedSample(bn) W[x] W[x] + w where x is the value of X in x return Normalize(W[X ]) function WeightedSample(bn, e) returns an event and a weight x an event with n elements; w 1 for i = 1 to 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 hapter
26 Likelihood weighting example P() loudy P(S ).10 P(R ) S R P(W S,R) w = 1.0 hapter
27 Likelihood weighting example P() loudy P(S ).10 P(R ) S R P(W S,R) w = 1.0 hapter
28 Likelihood weighting example P() loudy P(S ).10 P(R ) S R P(W S,R) w = 1.0 hapter
29 Likelihood weighting example P() loudy P(S ).10 P(R ) S R P(W S,R) w = hapter
30 Likelihood weighting example P() loudy P(S ).10 P(R ) S R P(W S,R) w = hapter
31 Likelihood weighting example P() loudy P(S ).10 P(R ) S R P(W S,R) w = hapter
32 Likelihood weighting example P() loudy P(S ).10 P(R ) S R P(W S,R) w = = hapter
33 Likelihood weighting analysis Sampling probability for WeightedSample is S W S (z, e) = Π l i = 1P (z i parents(z i )) Note: pays attention to evidence in ancestors only somewhere in between prior and posterior distribution loudy Weight for a given sample z, e is w(z, e) = Π m i = 1P (e i parents(e i )) Weighted sampling probability is S W S (z, e)w(z, e) = Π l i = 1P (z i parents(z i )) Π m i = 1P (e i parents(e i )) = P (z, e) (by standard global semantics of network) Hence likelihood weighting returns consistent estimates but performance still degrades with many evidence variables because a few samples have nearly all the total weight hapter
34 Approximate inference using MM State of network = current assignment to all variables. Generate next state by sampling one variable given Markov blanket Sample each variable in turn, keeping evidence fixed function MMAsk(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 = 1 to N do for each Z i in Z do sample the value of Z i in x from P(Z i mb(z i )) given the values of MB(Z i ) in x N[x] N[x] + 1 where x is the value of X in x return Normalize(N[X ]) an also choose a variable to sample at random each time hapter
35 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 hapter
36 MM example contd. Estimate P( = true, W et = true) Sample 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, 69 have = false ˆP( = true, W et = true) = Normalize( 31, 69 ) = 0.31, 0.69 heorem: chain approaches stationary distribution: longrun fraction of time spent in each state is exactly proportional to its posterior probability hapter
37 Markov blanket sampling Markov blanket of loudy is and Markov blanket of is loudy,, and W et loudy Probability given the Markov blanket is calculated as follows: P (x i mb(x i )) = P (x i parents(x i ))Π Zj hildren(x i )P (z j parents(z j )) Easily implemented in messagepassing 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) hapter
38 Summary Exact inference by variable elimination: polytime on polytrees, NPhard 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 hapter
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