Bayesian Networks
description
Transcript of Bayesian Networks
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Bayesian Networks• A causal probabilistic network, or Bayesian network, is an directed acyclic graph (DAG) where nodes represent variables and links represent dependency relations, e.g. of the type cause-effect, between variables and quantified by (conditional) probabilities
• Qualitative component + quantitative component
A
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Bayesian Networks
• Qualitative component : relations of conditional dependence / independence
I(A, B | C): A and B are independent given CI(A, B) = I(A, B | Ø): A and B are a priori independent
• Formal study of the properties of the ternary relation I
• A Bayesian network may encode three fundamental types of relations among neighbour variables.
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Qualitative Relations : type I
FGH
Ex: F: smoke, G: bronchitis, H: respiratory problems (dyspnea)
Relations:¬ I(F, H)
I(F, H | G)
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Qualitative Relations : type II
EFG
Ex: F: smoke, G: bronchitis,
E: lung cancer
Relations:¬ I(E, G)
I(E, G | F)
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Qualitative Relations : type III
B C E
Ex: C: alarm, B: movement detection,
E: rain
Relations: I(B, E)
¬ I(B, E | C)
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Probabilistic component
• Qualitative knowledge: a directed acyclic graph G (DAG)Nodes(G) = V = {X1, …, Xn} -- discrete variables --Edges(G) VxVParents(Xi) = {Xi : (Xj, Xi) Edges(G)}
• Probabilistic knowledge: P(Xi | parents(Xi))
These probabilities determine a joint probability distribution P over V = {X1, …, Xn}:
P(X1, …, Xn) = P(X1 | parents(X1)) · · · P(Xn | parents(Xn))
Bayesian Network = (G, P)
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Joint Distribution
• P(X1,X2,...Xn) = P(Xn|Xn-1,...X1) ... P(X3|X2,X1) P(X2|X1) P(X1).
• Independence relations of each variable Xi with the set of predecessor variables of the parents of Xi:
P(Xi | parents(Xi), Y1,.., Yk) = P(Xi | parents(Xi))
P(X1, X2, ..., Xn) = i=1,n P(Xi | parents(Xi))
• to have in each node Xi the conditional probability distribution P(Xi | parents(Xi)) is enough to determine the full joint probability distribution P(X1,X2,...,Xn)
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ExampleA
B
C
D
E
F
G
H
P(A): P(a) = 0.01P(B | A): P(b | a) = 0.05, P(b | ¬a) = 0.01P(C | B,E): P(c | b, e) = 1, P(c | b, ¬e) = 1, P(c | ¬b, e) = 1, P(c | ¬b, ¬e) = 0P(F): P(f) = 0.5P(D | C): P(d | c) = .98, P(d | ¬c) = 0.05P(E | F): P(e | f) = 0.1, P(e | ¬f) = 0.01P(G | F): P(g | f) = 0.6, P(g | ¬f) = 0.3P(H | C, G): P(h | c,g) =0.9 , P(h | c,¬g) = 0.7, P(h | ¬c,g) = 0.8, P(h | ¬c,¬g) = 0.1,
P(A,B,C,D,E,F,G,H) = P(D | C) P(H | C, G) P(C | B, E) P(G | F) P(E | F) P(F) P(B | A) P(A)
P(a,¬b,c,¬d,e,f,g,¬h) = P(¬d |c) P(¬h |c,g) P(c | ¬b,e) P(g | f) P(e | f) P(f) P(¬b | a) P(a) = (1- 0.98) (1-0.9) 1 0.6 0.1 0.5 (1-0.05) 0.01 = 5,7 10-7.
A: visit to Asia B: tuberculosisF: smoke E: lung cancerG: bronchitis C: B or ED: X-ray H: dyspnea
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D-separation relations and probabilistic independence
Goal: precesely determine which independence relations (graphically) are defined by one DAG.
Previous definitions:
• A path is a sequence of connected nodes in the graph. • A non directed path is a path without taking into account the directions of the arrows. • A “head-to-head” link in a node is a (non directed) path of the form xyw, the node y is clalled a “head-to-head” node.
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D-separation• A path c is called to be activated by a set of nodes Z if the following two conditions are satisfied:
1) Every node in c with links head-to-head is in Z or it has a descendent in Z.
2) Any other node in c does not belong to Z.Otherwise, the path c is called to be blocked by Z.
Definition. If X, Y and Z are three disjoint subsets of nodes disjunts in a DAG G, then Z d-separates X from Y, or equivalently X and Y are graphically independent given Z, when all the paths between any node from X and any node from Y are blocked by Z
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D-separationA
B C
G
E
D
Theorem. Let G be a DAG and let X,Y and Z be subsets of nodes such that X and Y are d-separated by Z. Then, X and Y are conditionally independent from Zfor any probability P such that (G, P) is a causal network over G, that is, s.t. P(X | Y,Z) = P(X | Z) and P(Y | X,Z) = P(Y | Z).
{B} and {C} are d-separated by {A}:
Path B-E-C: E,G {A} - {A} blocks the path B-E-C
Path B-A-C: - {A} blocks the path B-A-C
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Inference in Bayesian NetworksKnowledge about a domain encoded by a Bayesian network XB = (G, P).
Inference = updating probabilities: evidence E on values taken by some variables modify the probabilities of the rest of variables
P(X) --- > P’(X) = P(X | E)
Direct Method:
XB = < G = {A,B,C,D,E}, P(A,B,C,D,E) >
Evidence: A = ai, B = bjP ( a i , b j , c k , d m , e p )
m , p
∑
P ( a i , b j , c k , d m , e p )
k , m , p
∑
P(C = ck | A = ai, B = bj) =
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Inference in Bayesian Networks• Bayesian networks allow local computations, which exploit the indepence relations among variables explictly induced by the corresponding DAG of the networks.
• They allow updating the probability of a variable using only the probabilities of the immediat predecessor nodes (parents), and in this way, step by step to update the probabilities of all non-instantiated variables in the network ---> propagation methods
• Two main propagation methods:
• Pearl method: message passing over the DAG
• Lauritzen & Spiegelhalter method: previous transformation of the DAG in a tree of cliques
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Propagation method in trees of cliques
1) transformation of initial network in another graphical structure, a tree of cliques (subsets de nodes)
equivalent probabilistic information
BN = (G, P) ----> [Tree, P]
2) propagation algorithm over the new structure
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Graphical TransformationDefinition: a “clique” in a non-directed graph is a complete
and maximal subgraph
To transform a DAG G in a tree of cliques:
1) Delete directions in edges of G: G’
2) Moralization of G’: add edges between nodes with common sons in the original DAG G: G’’
3) Triangularization of G’’ : G*
4) Identification of the cliques in G*
5) Suitable enumeration of the cliques (Running Inters. Prop.)
6) Construction of the tree according to the enumeration
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Example (1)
A
B
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G
H A
B
C
D
E
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H
A
B
C
D
E
F
G
H
1)
2)
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Example (2): triangularizationA
B
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A
B
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A
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H
3) 3)
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Example (3): cliques
A
B
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H
A
B
C
D
E
F
G
H
Cliques:{A,B}, {B,C,E}, {E,F,G}, {C,E,G}, {C,G,H}, {C,D}
Cliques:4)
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Ordering of cliques
Enumeration of cliques Clq1, Clq2, …, Clqn such that the following property holds:
Running Intersection Property: for all i=1,…, n there exists j < i such that Si Clqj , where Si = Clqi(Clq1Clq2...Clqi-1).
This property is guaranteed if: (i) nodes of the graph are enumerated following the criterion of “maximum cardinality search”(ii) cliques are ordered according to the node of the clique with a highest ranking in the former enumaration.
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Example (4): ordering cliques
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Clq1 = {A,B}, Clq2 = {B,E,C}, Clq3 = {E,C,G}, Clq4 = {E,G,F}, Clq5 = {C,G,H}, Clq6 = {C,D}
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Tree Construction
Let [Clq1, Clq2, …, Clqn ] be an ordering satisfying R.I.P.
For each clique Clqi, define
Si = Clqi(Clq1Clq2...Clqi-1)Ri = Clqi-Si.
Tree of cliques:- (hyper) nodes: cliques- root: Clq1
- for each clique Clqi, its “father” candidates are
cliques Clqk with k < i and s.t. Si Clqk
(if more than one candidate, random selection)
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Example (5): trees S2 = Clq2 Clq1{Clq1
S3 = Clq3(Clq1Clq2){E,CClq2
S4 = Clq4(Clq1Clq2 Clq3){GClq3
S5 = Clq5(Clq1Clq2 Clq3.Clq4){C,GClq3
S6 = Clq6( Clq1Clq2 Clq3.Clq4Clq5){CClq2, Clq3, Clq5
Clq1
Clq2
Clq3
Clq4 Clq5Clq6
Clq1
Clq2
Clq3
Clq4 Clq5
Clq6
Clq1
Clq2
Clq3
Clq4 Clq5
Clq6
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Propagation Algorithm
• Potential Representation of the distribution P(X1, …, Xn):
([W1...Wp], ) is a potential representation of P, where the Wi
are subsets of V = {X1, …, Xn}, if P(V) =
• In a Bayesian network (G, P): P(X1, ..., Xn) = P(Xn| parents(Xn))·...· P(X1| parents(X1))
admits a potential representationP(X1, ..., Xn) = (Clq1) ·(Clq2) · ...·(Clqm)
with (Clqi)= ∏{P(Xj | parents(Xj)) | XjClqi, parents(Xj) Clqi ,
K ( W i )
i = 1
p
∏
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Propagation Algorithm (2)
Fundamental property of the potential representations:
• Let ([W1, ..., Wm], ) be a potential representation for P. Evidence: X3 = a and X5 = b.
• Problem: update the probabilitaty P’(X1, ..., Xn) = P(X1, ..., Xn| X3=a,X5 = b) ??
Define: W^i = Wi - {X3, X5} ^(W^i) = (Wi (X3=a, X5=b))
([W^1, ..., W^m], ^) is a potential representation for P'.
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Example (6): potentialsClq1
Clq2
Clq3
Clq4 Clq5
Clq6
Clq1 = {A,B}, Clq2 = {B,E,C}, Clq3 = {E,C,G}, Clq4 = {E,G,F}, Clq5 = {C,G,H}, Clq6 = {C,D}
A
B
C
D
E
F
G
H
(Clq1) = P(A)· P(B | A) (Clq2) = P(C | B,E), (Clq3) = 1 (Clq4) = P(F).P(E | F).P(G | F), (Clq5) = P(H | C, G)(Clq6) = P(D | C)
P(A,B,C,D,E,F,G,H) = P(D | C) P(H | C, G) P(C | B, E) P(G | F) P(E | F) P(F) P(B | A) P(A)
P(A,B,C,D,E,F,G,H) = (Clq1) • …. • (Clq6)
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Example(6): potentials
(Clq1) = P(A)· P(B | A)(a,b) = P(a) · P(b | a) = 0.005(¬a,b) = P(¬a) · P(b | ¬a) = 0.0099(a,¬b) = P(a) · P(¬b | a) = 0.0095(¬a,¬b) = P(¬a) · P(¬b | ¬a) = 0.9801
(Clq5) = P(H | C, G)(c,g,h) = P(h | c,g) = 0.9 (c,g,¬h) = P(¬h | c,g) = 0.1(c,¬g,h) = P(h | c,¬g) = 0.7 (c,¬g,¬h) = P(¬h | c,¬g) = 0.3(¬c,g,h) = P(h | ¬c,g) = 0.8 (¬c,g,¬h) = P(¬h | ¬c,g) = 0.2(¬c,¬g,h) = P(h | ¬c,¬g) = 0.1 (¬c,¬g,¬h) = P(¬h | ¬c,¬g) = 0.9
…
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Propagation algorithm: theoretical resultats
Causal network (G, P)([Clq1, ..., Clqp], ) is a potential representation for P
1) P(Clqi) = P(Ri|Si).P(Si)
2) P(Rp|Sp) = , where is the marginal
of the function with respect to the variables of Rp.
3) If father(Clqp) = Clqj, then ([Clq1,...Clqp-1], ') is a potential representation for the marginal distribution of P(V-Rp) where:
'(Clqi)=Clqi) for all i≠j, i < p'(Clqj)=Clqj)
( Clq p )
ψ ( Clq p )
R p
∑
( Clq p )
R p
∑
( Clq p )
R p
∑
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Propagation algorithm: step by step (2)
Goal: to compute P(Clqi) for all cliques.
Two graph traversals: one bottom-up and one top-down
BU) start with clique Clqp . Combining properties 2 i 3 we have, an iterative form of computing the conditional distributions P(Ri|Si) in each clique until reaching the root clique Clq1.
Root: P(Clq1)=P(R1|S1).
TD) P(S2)= , and from there P(Si)=
--we can always compute in a clique Clqi the distribution P(Si) whenever we have already computed the distribution of its father clique Clqj --
P ( Clq 1 )
Clq 1 − S 2
∑P ( Clq j )
Clq j − S i
∑
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Clq1
Clq2
Clq3
Clq4 Clq5
Clq6
Clq1
Clq2
Clq3
Clq4 Clq5
Clq6
P(Ri | Si)
P(Si)
P(Clqi) = P(Ri,Si) = P(Ri | Si) P(Si)
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Clqi P(Ri|Si) = =
(Clqi)Ri(Clqi)
(Clqi)
(Clqi)’(Clqi) =
(Clqi) j(Sj) k(Sk) Clqi
Clqj Clqk
Clqi
Clqj Clqk
(Clqi) i(Si)
Case 1)
Case 2)
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Clq1
Clq2
Clq3
Clq4 Clq5
Clq6
6(S6)
5(S5) 4(S4)
3(S3)
2(S2)
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Example (7)
A) Bottom-up traversal: passing k(Sk) = Rk(Clqk),
Clique Clq6 = {C,D} (R6= {D}, S6 = {C}).
P(R6|S6) = P(D | C) =
6(c) = (c, d) + (c, ¬d) = 0.98 + 0.02 = 16(¬c) = (¬c, d) + (¬c, ¬d) = 0.05 + 0.95 = 1,
P(d | c) = P(¬d | c) = 0.02
P(d | ¬c) = P(¬d | ¬c) = 0.95
( R6
, S6
)
λ6
( S6
)
( c , d )
λ6
( c )
=
0 . 98
1
= 0 . 98
( ¬ c , d )
λ ( ¬ c )
=
0 . 05
1
= 0 . 05
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Example (7)
Clique Clq5 = {C, G, H} (R5 = {H}, S5 = {C, G}).
This node is clique Clq6’s father. According to point [3], we modify the potential function of the clique Clq5:
'(Clq5)=Clq5)
P(R5 | S5) = P(H | C,G) =
where 5(C,G) =
5(c,g) = '(c, g, h) + '(e, g, ¬h) = 0.9 + 0.1 = 15(c,¬g) = '(c, ¬g, f) + '(c, ¬g, ¬h) = 0.7 + 0.3 = 15(¬c,g) = … = 5(c,¬g) = ...= 1
( Clq6
)
R6
∑ = ψ ( Clq 5 ) ⋅ λ6
( S6
)
' ( Clq5
)
ψ ' ( Clq 5 )
R 5
∑
=
ψ ' ( R5
, S5
)
λ5
( S5
)
' ( C , G , H )
H
∑
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Exemple (7)
Clique Clq3 = {E,C,G} (R3 = {G}, S3 = {E,C})
Clq1
Clq2
Clq3
Clq4 Clq5
Clq6
Clq3 is father of two cliques: Clq4 and Clq5, both already processed
'(Clq3) = Clq3) R(Clq4) · R(Clq5)
= (Clq5) · 4(S4) · 5(S5)
'(E,C,G) = E,C,G) · 4(E,G) · 5(C,G)
P(R3 | S3) = P(G | E, C) =
where 3(E,C) =
' ( Clq3
)
ψ ' ( Clq3
)
R 3
∑
=
ψ ' ( R3
, S3
)
λ3
( S3
)
' ( E , C , G )
G
∑
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Example (7)Root: Clique Clq1 = {A, B} (R1 = {A, B}, S1 = ).
'(A,B)=A,B) · 2(B)
P(R1) = P(R1 | S1) =
where 1 = '(a,b) + '(a,¬b)+'(¬a,b)+'(¬a,¬b).
P(A,B) = A,B) : P(a,b) = 0.005, P(a, ¬b) = 0.0095, P(¬a, b) = 0.099, P(¬a, ¬b) =
0.9801
' ( Clq1
)
ψ ' ( Clq 1 )
R 1
∑
=
ψ ' ( R1
)
λ1
( ∅ )
=
ψ ' ( A , B )
λ1
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Clqi
Clqj Clqk
P(Clqi) = P(Ri|Si).P(Si)
P(Sk) = Clqi -Sk P(Clqi) = i(Sk) P(Sj) = Clqi -Sj P(Clqi) = i(Sj)
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Clq1
Clq2
Clq3
Clq4 Clq5
Clq6
5(S6)
3(S5) 3(S4)
2(S3)
1(S2)
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Example (7)
A) Top-down traversal:
Clique Clq2 = {B,E,C} (R2 = {E,C}, S2 = {B}).
P(B) = P(S2) =
P(b) = P(a, b) + P(¬a, b) = 0.005 + 0.099 = 0.104 , P(¬b) = P(a, ¬b) + P(¬a, ¬b) = 1- 0.104 = 0.896
*** P(Clq2) = P(R2 | S2) · P(S2)
P ( Clq 1 )
Clq 1 − S 2
∑
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Example (7)
Clique Clq3 = {E,C,G} (R3 = G, S3 = {E,C}).
we have to compute P(S3) i P(Clq3)
Clique Clq4 ={E, G, F} (R4 = {F}, S4 = {E,G}).
we have to compute P(S4) i P(Clq4)
Clique Clq5 = {C, G, H} (R5 = {H}, S5 = {C, G}).
we have to compute P(S5) i P(Clq5)
Clique Clq6 = {C,D} (R6= {D}, S6 = {C}).
we have to compute P(S6) i P(Clq6)
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Summary
Given a Bayesian network BN = (G, P), we have seen how
1) To transform G into a tree of cliques and factorize P as
P(X1, ..., Xn) = (Clq1) ·(Clq2) ·...·(Clqm)
where (Clqi)= ∏{P(Xj | parents(Xj)) | XjClqi, parents(Xj) Clqi,
2) To compute the probabilty distributions P(Clqi) with a propagation algorithm, and from there, to compute the probabilities P(Xj) for Xj Clqi, by marginalization.
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Probability updating
It remains to see how to perform inference,
i.e. how to update probabilities P(Xj) when some information (evidence E) is available about some variables:
P(Xj) --- > P*(Xj) = P(Xj | E)
The updating mechanism is based in a fundamental property of the potential representations when applied to P(X1, ..., Xn) and its potential representation in terms of cliques:
P(X1, ..., Xn) = (Clq1) ·(Clq2) ·...·(Clqm)
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Updating mechanismRecall:
• Let ([Clq1, ..., Clqm], ) be a potential representation for P(X1, …, Xn).
• We observe: X3 = a and X5 = b.
• Actualització de la probabilitat: P*(X1,X2,X4,X6,..., Xn) = P(X1, ...,Xn| X3=a,X5 = b)
Define: Clq^i = Clqi - {X3, X5} ^(Clq^i) = (Clqi (X3=a, X5=b))
([Clq^1, ..., Clq^m], ^) is a potential representation for P*.
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Updating mechanism
Based on three steps:
A) build the new tree of cliques obtained by deleting from the original tree the instantiated variables,
B) re-compute the new potential functions ^ corresponding to the new cliques and, finally,
C) apply the propagation algorithm over the new tree of cliques and potential functions.
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A,B
B,E,C
E,C,G
E,G,F C,G,H
C,D
Clq1
Clq2
Clq3
Clq4
Clq5
Clq6
B
B,E,C
E,C,G
E,G,F C,G
C,D
Clq’1
Clq’2
Clq’3
Clq’4
Clq’5
Clq’6
A = a, H = bP(Xj) P*(Xj) = P(Xj | X=a,H=h)
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A = a, H = b
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A = a, H = b
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P(D = d | A = a, H = h) ?