Inference in Bayesian Nets Objective: calculate posterior prob of a variable x conditioned on evidence Y and marginalizing over Z (unobserved vars) Exact methods: Enumeration Factoring Variable elimination Factor graphs (read 8.4.2-8.4.4 in Bishop, p. 398-411) Belief propagation Approximate Methods: sampling (read Sec 14.5)

from: Inference in Bayesian Networks (DAmbrosio, 1999) Factors A factor is a multi-dimensional table, like a CPT fAJM(B,E) 2x2 table with a number for each combination of B,E Specific values of J and M were used A has been summed out p(j|a) p(j|a) p(j|a) p(j|a) f(J,A)=P(J|A) is 2x2: fJ(A)=P(j|A) is 1x2: {p(j|a),p(j|a)} Use of factors in variable elimination: Pointwise product given 2 factors that share some variables: f1(X1..Xi,Y1..Yj), f2(Y1..Yj,Z1..Zk)

resulting table has dimensions of union of variables, f1*f2=F(X1..Xi,Y1..Yj,Z1..Zk) each entry in F is a truth assignment over vars and can be computed by multiplying entries from f1 and f2 A B f1(A,B) B C f2(B,C) A

B C F(A,B,C) T T 0.3 T T 0.2 T

T T 0.3x0.2 T F 0.7 T F 0.8

T T F 0.3x0.8 F T 0.9 F T 0.6

T F T 0.7x0.6 F F 0.1 F F

0.4 T F F 0.7x0.4 F T T 0.9x0.2 F

T F 0.9x0.8 F F T 0.1x0.6 F F

F 0.1x0.4 Factor Graph Bipartite graph variable nodes and factor nodes one factor node for each factor in joint prob. edges connect to each var contained in each factor F(B) F(E) B E

F(A,B,E) A F(J,A) J F(M,A) M Message passing Choose a root node, e.g. a variable whose marginal prob you want, p(A) Assign values to leaves For variable nodes, pass =1 For factor nodes, pass prior: f(X)=p(X)

Pass messages from var node v to factor u Product over neighboring factors Pass messages from factor u to var node v sum out neighboring vars w Terminate when root receives messages from all neighbors or continue to propagate messages all the way back to leaves Final marginal probability of var X: product of messages from each neighboring factor; marginalizes out all variables in tree beyond neighbor Conditioning on evidence: Remove dimension from factor (sub-table) F(J,A) -> FJ(A)

Belief Propagation (this figure happens to come from http://www.pr-owl.org/basics/bn.php) see also: wiki, Ch. 8 in Bishop PR&ML Computational Complexity Belief propagation is linear in the size of the BN for polytrees Belief propagation is NP-hard for trees with cycles Inexact Inference Sampling Generate a (large) set of atomic events (joint variable assignments) <-e,b,a,j,m> ...

Answer queries like P(J=t|A=f) by averaging how many times events with J=t occur among those satisfying A=f Direct sampling create an independent atomic event for each var in topological order, choose a value conditionally dependent on parents 1. 2. 3. 4. sample from p(Cloudy)=<0.5,0.5>; suppose T

sample from p(Sprinkler|Cloudy=T)=<0.1,0.9>, suppose F sample from P(Rain|Cloudy=T)=<0.8,0.2>, suppose T sample from P(WetGrass|Sprinkler=F,Rain=T)=<0.9,0,1>, suppose T event: repeat many times in the limit, each event occurs with frequency proportional to its joint probability, P(Cl,Sp,Ra,Wg)= P(Cl)*P(Sp|Cl)*P(Ra|Cl)*P(Wg|Sp,Ra) averaging: P(Ra,Cl) = Num(Ra=T&Cl=T)/|Sample| Rejection sampling to condition upon evidence variables e, average over samples that satisfy e

P(j,m|e,b) <-e,b,a,j,m> <-e,-b,-a,-j,m> <-e,-b,a,-j,-m> <-e,-b,a,j,-m> ... Likelihood weighting sampling might be inefficient if conditions are rare P(j|e) earthquakes only occur 0.2% of the time, so can only use ~2/1000 samples to determine frequency of JohnCalls during sample generation, when reach an evidence variable ei, force it to be known value accumulate weight w= p(ei|parents(ei))

now every sample is useful (consistent) when calculating averages over samples x, weight them: P(j|e) = consistent w(x)= Gibbs sampling (MCMC) start with a random assignment to vars set evidence vars to observed values iterate many times... pick a non-evidence variable, X define Markov blanket of X, mb(X) parents, children, and parents of children re-sample value of X from conditional distrib. P(X|mb(X))=P(X|parents(X))* P(y|parents(X)) for ychildren(X) generates a large sequence of samples, where each might flip a bit from previous sample

in the limit, this converges to joint probability distribution (samples occur for frequency proportional to joint PDF) Other types of graphical models Hidden Markov models Gaussian-linear models Dynamic Bayesian networks Learning Bayesian networks known topology: parameter estimation from data structure learning: topology that best fits the data Software BUGS Microsoft