Bayesian Networks Representation and Reasoning

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1 ayesian Networks Representation and Reasoning Marco F. Ramoni hildren s Hospital Informatics Program Harvard Medical School (2003) Harvard-MIT ivision of Health Sciences and Technology HST.951J: Medical ecision Support

2 Introduction ayesian network are a knowledge representation formalism for reasoning under uncertainty. ayesian network is a direct acyclic graph encoding assumptions of conditional independence. In a ayesian network, nodes are stochastic variables and arcs are dependency between nodes. ayesian networks were designed to encode explicitly encode deep knowledge rather than heuristics, to simplify knowledge acquisition, provide a firmer theoretical ground, and foster reusability.

3 Graph graph (network) G(N,L) is defined by: Nodes: finite set N = {,,...} of nodes (vertices). rcs: set L of arcs (edges): ordered pair of nodes. Set L is a subset of all possible pairs of nodes N. L={(,),(,),(,)} L={(,),(,)} L={(,),(,),(,),(,),(,),(,)}

4 Graph irected Types of Graph Undirected onnected isconnected cyclic yclic Singly onnected (Tree) Simple Tree Polytree Multiply onnected

5 irection irection of a link: irected: if (,) is in N, then (,) is not in N. Undirected: if (,) is in N, then (,) is in N. Note: The link should be «. haracters: djacent set: the nodes one step away from : dj()={ (,) L}. Path: The set of n nodes X i from to via links: Loop: closed path: X 1 = X n. cyclic graph: graph with no cycles.

6 irected Graphs Parent: is parent of if there is a directed link fi. Family: The set made up by a node and its parents. ncestor: is ancestor of if exists a path from to. ncestral set: set of nodes containing their ancestors. ycle: cycle is a closed loop of directed links. ssociated acyclic graph: The undirected graph obtained by dropping the direction of links. Moral graph: The undirected graph obtained by. Marring the parents of a common child. ropping the directions of the links.

7 Trees Tree: If every pair of nodes there is at most one path. Simple Tree: ach node has at most one parent. PolyTree: Nodes can have more than one parent. Multiply onnected Graph: graph where at least one pair of nodes has more than one path. Note: The associated undirected graph has a loop.

8 ayesian Networks Qualitative: dependency graph made by: Node: a variable X, with a set of states {x 1,,x n }. rc: a dependency of a variable X on its parents P. Quantitative: The distributions of a variable X given each combination of states p i of its parents P. p() p() Y Y O O p() p() L L H H I =ge ge; ; =ducation ducation; ; I=Income Income I I p(i,) p(i,) Y Y L L L L Y Y L L H H Y Y H H L L Y Y H H H H O O L L L L O O L L H H O O H H L L O O H H H H

9 Independence Perfect dependence between isease and Test: Test isease Test isease Independence between isease and Test: Test isease Test isease xercise: ompute the PT for Test given isease.

10 Why o We are? Independence simplifies models: if two variables are independent, I do not need to model their interaction but I can reason about them separately. In this form of independence, called marginal independence, however, a variable will tell me nothing about another variable, by design. There is another, more useful, form of independence, which maintains the connection between variables but, at the same time, breaks down the whole system in separate regions: conditional independence. This is independence used by ayesian networks.

11 onditional Independence When two variables are independent given a third, they are said to be conditionally independent. p( )=p( )/p( )=p( ). Literacy Literacy T-shirt Yes No Small Large ge T-shirt Yes No <5 Small <5 Large >5 Small >5 Large

12 ayesian Networks ayesian networks use graphs to capture these statement of conditional independence. ayesian network (N) is defined by a graph: Nodes are stochastic variables. Links are dependencies. No link means independence given a parent. There are two components in a N: Qualitative graphical structure. Quantitative assessment of probabilities. ge T-shirt Literacy

13 ecomposition Ns decompose the joint probability distribution with the graph of conditional independence. Therefore, the graphical structure factorizes the joint probability distribution: p( ) = p( ) p( ) p().

14 Markov quivalence ifferent network structures may encode the same conditional independence statements: and are conditionally independent given. can be encoded by 3 different network structures. In all these network structures, the information flow running between and along the direction of the arrows is mediated by the node.

15 xample ackground knowledge: General rules of behavior. p(ge=<5)=0.3 p(t-shirt=small ge=<5)=0.5 p(t-shirt=small ge=>5)=0.3 p(literacy=yes ge=>5)=0.6 p(literacy=yes ge=<5)=0.2. vidence: Observation p(t-shirt=small). Solution: The posterior probability distribution of the unobserved nodes given evidence: p(literacy T-shirt=small) and p(ge T-shirt=small). p(ge=<5,t-shirt=small,literacy=yes) p(ge=<5,t-shirt=small,literacy=no) p(ge=<5,t-shirt=large,literacy=yes) ge p(ge=<5,t-shirt=large,literacy =no) p(ge=>5,t-shirt=small,literacy=yes) p(ge=>5,t-shirt=small,literacy=no) p(ge=>5,t-shirt=large,literacy=yes) p(ge=>5,t-shirt=large, Literacy=no). T-shirt Literacy

16 Reasoning omponents of a problem: Knowledge: graph and numbers. vidence: e={c and g}. Solution: p(d c,g)=? Note: Lower case is an instance. F G p() p() p( ) F p() p(f ) p(,) G p(g,)

17 ompute the Joint Probability istribution: rute Force p(a,b,c,d,e,f,g)=p(a)p(b)p(c d)p(d a,b)p(e)p(f d)p(g d,e). Marginalize out the variable of interest: p(d)=s p(a,b,c,e,f,g). Note: We have replaced with, ost: 2 n probabilities (2 6 = 64)

18 ecomposition ecomposition: breaks the N into two Ns: p(d)= S p(a)p(b)p(c a)p(d a,b)p(e)p(f d)p(g d,e)=. = (S p(a)p(b)p(c a)p(d a,b)) (S p(e)p(f d)p(g d,e)). Saving: We move from 64 to =16, and most of all the terms move from 7 to 4 and from 7 to 3. -separation: the basic idea is based on a property of graphs called d-separation (directed-separation).

19 Propagation in Polytrees In a polytree, each node breaks the graph into two independent sub-graphs and evidence can be independently propagated in the two graphs: +: evidence coming from the parents (+ = {c}). -: evidence coming from the children (- = {g}). F G

20 Message Passing Message passing algorithm (Kim & Pearl 1983) is a local propagation method for polytrees. The basic idea is that p(d) is actually made up by parent component p(d) and a south component l(d). The basic idea is to loop and pass p and l messages between nodes until no message can be passed. In this way, the propagation is entirely distributed and the computations are locally executed in each node.

21 lgorithm Input: N with a set of variables X and a set of evidential statements e = {=a,=b, }. Output: onditional probability distribution p(x e) for each non evidential variable X. Initialization Step: ach evidential variable X, if x e p(x)=1, else p(x)=0. if x e l(x)=1, else l(x)=0. ach non evidential root variable X, p(x) = p(x). ach non evidential childless variable X, l(x)=1.

22 lgorithm II Iteration Step (on non evidential variables X/e): If X has all the p-messages from its parents, p(x). If X has all the l-messages from its children, l(x). If p(x), for each child, if l -messages from all other children are in, send p-message to child. If l(x), for each parent, if p-messages from all other parents are in, send l-message to parent. Repeat until no message is sent. losure: For each X/e, compute b(x)= p(x) l(x). For each X/e, compute p(x)= b(x)/s l(x i ).

23 Properties istributed: ach node does not need to know about the others when it is passing the information around. Parallel architecture: ach node can be imagined as a separate processor. omplexity: Linear in the number of nodes. Limitations: onfined to a restricted class of graphs and, most of all, unable to represent an important class of problems. Importance: Proof of feasibility - ayesians are not just dreamers after all.

24 Multiply onnected N When the N is a multiply connected graph. The associated undirected graph contains a loop. ach node does not break the network into 2 parts. Information may flow through more than one paths. Pearl s lgorithm is no longer applicable. F G F G

25 Main stream methods: onditioning Methods. lustering Methods. Methods The basic strategy is: Turn multiply connected graph in something else. Use Pearl s algorithm to propagate evidence. Recover the conditional probability p(x e) for X. Methods differ in the way in which. What they transform the graph into. The properties they exploit for this transformation.

26 onditioning Methods The transformation strategy is: Instantiate a set of nodes (cutset) to cut the loops. bsorb evidence and change the graph topology. Propagate each N using Pearl s algorithm. Marginalize with respect to the loop cutset. =e F G F G

27 lgorithm Input: a (multiply connected) N and evidence e. Output: the posterior probability p(x e) for each X. Procedure: 1. Identify a loop cutset =( 1,, n ). 2. For each member of combinations c=(c 1,, c n ). Generate a polytree Ns for each c. Use Pearl s lgorithm to compute p(x e,c 1,,c n ). ompute p(c 1,, c n e) = p(e c 1,,c n )p(c 1,,c n ) /p(e). 3. For each node X, a=p(x e) S c p(x e,c 1,,c n )p(e c 1,,c n )p(c 1,,c n ), ompute p(x e)= a/s x p(x).

28 omplexity The computational complexity is exponential in the size of the loop cutset, as we must generate and propagate a N for each combination of states of the loop cutset. The identification of the minimal loop cutset of a N is NP-hard, but heuristic methods exist to make it feasible. The computational complexity is a problem common to all methods moving from polytrees to multiply connected graphs.

29 xample Multiply connected N No evidence p() p( ) p( ) p( ) F p(f ) F p(,)

30 Loop cutset: {}. p(=0)=p(=0 =0)p(=1) + p(=0 =1)p(=1) xample F F F

31 lustering Methods The basic strategy (Lauritzen & Spiegelhalter 1988) is: 1. onvert a N in a undirected graph coding the same conditional independence assumptions. 2. nsure the resulting graph is decomposable. 3. This operation clusters nodes in locally independent subgraphs (cliques). 4. These cliques are joint to each other via a single nodes. 5. Produce a perfect numbering of nodes. 6. Recursively propagate evidence.

32 Markov Networks Markov network is a based on undirected graphs: N : G = Markov Network : Undirected Graph. Markov networks encode conditional independence assumptions (as Ns) using a Undirected Graph: 1. link between and means dependency. 2. variable is independent of all not adjacent variables given the adjacent ones. xample: is independent from (,,) given.

33 ecomposable ecomposable Markov networks lead to efficiency: Markov network is said to be decomposable when it contains no cycle with longer than 3 (there is no unbroken cycle with more than 3 nodes). The joint probability distribution of the graph can be factorized by the marginal distributions of the cliques: clique is the largest sub-graph in which nodes are all adjacent to each other. Therefore, a clique cannot be further simplified by conditional independence assumptions.

34 Triangulation When a Markov network is not decomposable, we triangulate the graph by including the missing links. The product of the joint probability of each clique, divided by the product of their intersection: p(a,b,c)=p(c a)p(b a)p(a). Moralize 1.Marry parents 2.rop arrows

35 Reading Independence The translation method via moralization reads the conditional independence statements in N. Gs cannot encode any arbitrary set of conditional independence assumptions. I(, (,)) I(, (,))

36 Propagation ompile the N into a moralized Markov network. Maximum cardinality search: For each clique Q compute p(q e). Within each cluster, marginalize p(x e)

37 Who is the Winner? lustering is also NP-complete. The source of computational complexity is the size of the larger clique in the graph. Global conditioning (Shachter, ndersen & Szolovits 1994) shows that: 1. onditioning is a special case of lustering. 2. onditioning is better at trading off memory-time. 3. onditioning is better suited for parallel implementations.

COMPSCI 276 Fall 2007

COMPSCI 276 Fall 2007 Exact Inference lgorithms for Probabilistic Reasoning; OMPSI 276 Fall 2007 1 elief Updating Smoking lung ancer ronchitis X-ray Dyspnoea P lung cancer=yes smoking=no, dyspnoea=yes =? 2 Probabilistic Inference