Bayesian Belief Network
•
The decomposition of large probabilistic domains into
weakly connected subsets via conditional
independence is one of the most important
developments in the recent history of AI
•
This can work well, even the assumption is not true!
)
,
,
(
)
(
)
,
,
,
(
)
(
)
(
)
(
cavity
catch
toothache
P
cloudy
Weather
P
cloudy
Weather
cavity
catch
toothache
P
b
P
a
P
b
a
P
v
NB
Naive Bayes assumption:
which gives
Bayesian networks
Conditional Independence
Inference in Bayesian Networks
Irrelevant variables
Constructing Bayesian Networks
Aprendizagem Redes Bayesianas
Examples

Exercisos
Naive Bayes assumption of conditional
independence too restrictive
But it's intractable without some such
assumptions...
Bayesian Belief networks describe conditional
independence among
subsets
of variables
allows combining prior knowledge about
(in)dependencies among
variables with observed training data
Bayesian networks
A simple, graphical notation for conditional independence
assertions and hence for compact specification of full joint
distributions
Syntax:
a set of nodes, one per variable
a directed, acyclic graph (link
≈
"directly influences")
a conditional distribution for each node given its parents:
P
(X
i
 Parents (X
i
))
In the simplest case, conditional distribution represented as a
conditional probability table
(CPT) giving the distribution over
X
i
for each combination of parent values
Bayesian Networks
Bayesian belief network allows a
subset
of the
variables conditionally independent
A graphical model of causal relationships
Represents
dependency
among the variables
Gives a specification of joint probability distribution
X
Y
Z
P
Nodes: random variables
Links: dependency
X,Y are the parents of Z, and Y is the
parent of P
No dependency between Z and P
Has no loops or cycles
Conditional Independence
Once we know that the patient has cavity we do
not expect the probability of the probe catching to
depend on the presence of toothache
Independence between a and b
)

(
)

(
)

(
)

(
cavity
toothache
P
catch
cavity
toothache
P
cavity
catch
P
toothache
cavity
catch
P
)
(
)

(
)
(
)

(
b
P
a
b
P
a
P
b
a
P
Example
Topology of network encodes conditional independence assertions:
Weather
is independent of the other variables
Toothache
and
Catch
are conditionally independent given
Cavity
Bayesian Belief Network: An
Example
Family
History
LungCancer
PositiveXRay
Smoker
Emphysema
Dyspnea
LC
~LC
(FH, S)
(FH, ~S)
(~FH, S)
(~FH, ~S)
0.8
0.2
0.5
0.5
0.7
0.3
0.1
0.9
Bayesian Belief Networks
The
conditional probability table
for the variable LungCancer:
Shows the conditional probability
for each possible combination of its
parents
n
i
Z
Parents
i
z
i
P
zn
z
P
1
))
(

(
)
,...,
1
(
Example
I'm at work, neighbor John calls to say my alarm is ringing, but neighbor
Mary doesn't call. Sometimes it's set off by minor earthquakes. Is there a
burglar?
Variables:
Burglary
,
Earthquake
,
Alarm
,
JohnCalls
,
MaryCalls
Network topology reflects "causal" knowledge:
A burglar can set the alarm off
An earthquake can set the alarm off
The alarm can cause Mary to call
The alarm can cause John to call
Belief Networks
Burglary
P(B)
0.001
Earthquake
P(E)
0.002
Alarm
Burg.
Earth.
P(A)
t
t
.95
t
f
.94
f
t
.29
f
f .001
JohnCalls
MaryCalls
A P(J)
t .90
f .05
A P(M)
t .7
f .01
Full Joint Distribution
))
(

(
)
,...,
(
1
1
i
n
i
i
n
X
parents
x
P
x
x
P
00062
.
0
998
.
0
999
.
0
001
.
0
7
.
0
9
.
0
)
(
)
(
)

(
)

(
)

(
)
(
e
P
b
P
e
b
a
P
a
m
P
a
j
P
e
b
a
m
j
P
Compactness
A CPT for Boolean
X
i
with
k
Boolean parents has
2
k
rows for the
combinations of parent values
Each row requires one number
p
for
X
i
= true
(the number for
X
i
=
false
is just
1

p
)
If each variable has no more than
k
parents, the complete network requires
O(n
∙
2
k
) numbers
I.e., grows linearly with
n
, vs.
O(2
n
)
for the full joint distribution
For burglary net, 1 + 1 + 4 + 2 + 2 = 10 numbers (vs. 2
5

1 = 31)
Inference in Bayesian Networks
How can one infer the (probabilities of)
values of one or more network variables,
given observed values of others?
Bayes net contains all information needed
for this inference
If only one variable with unknown value,
easy to infer it
In general case, problem is NP hard
Example
In the burglary network, we migth observe
the event in which
JohnCalls=true
and
MarryCalls=true
We could ask for the probability that the
burglary has occured
P(BurglaryJohnCalls=ture,MarryCalls=true)
Remember

Joint distribution
Normalization
4
.
0
,
6
.
0
08
.
0
,
12
.
0
)

(
),

(
)
(
)

(
)

(
)

(
)

(
1
x
y
P
x
y
P
Y
P
Y
X
P
X
Y
P
x
y
P
x
y
P
Normalization
•
X is the query variable
•
E evidence variable
•
Y remaining unobservable variable
•
Summation over all possible y (all possible values of the
unobservable varables Y)
P(BurglaryJohnCalls=ture,MarryCalls=true)
•
The hidden variables of the query are
Earthquake
and
Alarm
•
For
Burglary=true
in the Bayesain network
To compute we had to add four terms,
each computed by multipling five numbers
In the worst case, where we have to sum
out almost all variables, the complexity of
the network with
n
Boolean variables is
O(n2
n
)
P(b)
is constant and can be moved out,
P(e)
term can be moved outside summation
a
JohnCalls=true
and
MarryCalls=true,
the probability
that the burglary has occured is aboud 28%
Computation for
Burglary=true
Variable elimination algorithm
•
Eliminate repeated calculation
•
Dynamic programming
Irrelevant variables
•
(X query variable, E evidence variables)
Complexity of
exact inference
The burglary network belongs to a family of
networks in which there is
at most one
undiracted path
between tow nodes in the
network
These are called singly connected networks or
polytrees
The time and space complexity of exact
inference in polytrees is linear in the size of
network
Size is defined by the number of CPT entries
If the number of parents of each node is bounded by
a constant, then the complexity will be also linear in
the number of nodes
For multiply connected networks variable
elimination can have exponentional time
and space complexity
Constructing Bayesian Networks
A Bayesian network is a correct
representation of the domain only if each node
is conditionally independent of its
predecessors in the ordering, given its parents
P(MarryCallsJohnCalls,Alarm,Eathquake,Bulgary)=P(MaryCallsAlarm)
Conditional Independence
relations in Bayesian networks
The toopological semantics is given either
of the spqcifications of DESCENDANTS
or MARKOV BLANKET
Local semantics
Example
JohnCalls
is indipendent of
Burglary
and
Earthquake
given the value of
Alarm
Example
Burglary
is indipendent of
JohnCalls
and
MaryCalls
given
Alarm
and
Earthquake
Constructing Bayesian
networks
1. Choose an ordering of variables
X
1
, … ,
X
n
2. For
i
= 1 to
n
add
X
i
to the network
select parents from
X
1
, … ,X
i

1
such that
P
(X
i
 Parents(X
i
)) =
P
(X
i
 X
1
, ... X
i

1
)
This choice of parents guarantees:
P
(X
1
, … ,X
n
)
=
π
n
i =1
P
(X
i
 X
1
, … , X
i

1
)
(chain rule)
=
π
n
i =1
P
(X
i
 Parents(X
i
))
(by construction)
The compactness of Bayesian networks is an
example of locally structured systems
Each subcomponent interacts directly with only
bounded number of other components
Constructing Bayesian networks is difficult
Each variable should be directly influenced by only a
few others
The network topology reflects thes direct influences
Suppose we choose the ordering
M, J, A, B, E
P
(J  M) =
P
(J)?
Example
Suppose we choose the ordering
M, J, A, B, E
P
(J  M) =
P
(J)?
No
P
(A  J, M) =
P
(A  J)
?
P
(A  J, M) =
P
(A)
?
No
P
(B  A, J, M) =
P
(B  A)
?
P
(B  A, J, M) =
P
(B)
?
Example
Suppose we choose the ordering M, J, A, B, E
P
(J  M) =
P
(J)?
No
P
(A  J, M) =
P
(A  J)
?
P
(A  J, M) =
P
(A)
?
No
P
(B  A, J, M) =
P
(B  A)
?
Yes
P
(B  A, J, M) =
P
(B)
?
No
P
(E  B, A ,J, M) =
P
(E  A)
?
P
(E  B, A, J, M) =
P
(E  A, B)
?
Example
Suppose we choose the ordering M, J, A, B, E
P
(J  M) =
P
(J)?
No
P
(A  J, M) =
P
(A  J)
?
P
(A  J, M) =
P
(A)
?
No
P
(B  A, J, M) =
P
(B  A)
?
Yes
P
(B  A, J, M) =
P
(B)
?
No
P
(E  B, A ,J, M) =
P
(E  A)
?
No
P
(E  B, A, J, M) =
P
(E  A, B)
?
Yes
Example
Example contd.
Deciding conditional independence is hard in noncausal directions
(Causal models and conditional independence seem hardwired for humans!)
Network is less compact: 1 + 2 + 4 + 2 + 4 = 13 numbers needed
Some links represent tenous relationship that require difficult and unnatural
probability judgment, such the probability of
Earthquake
given
Burglary
and
Alarm
Aprendizagem Redes Bayesianas
Como preencher as entradas numa Tabela de Probabilidade
Condicional
1º Caso:
Se a estrutura da rede bayesiana fôr conhecida, e todas as
variavéis podem ser observadas do conjunto de treino.
Então:
Entrada (i,j) =
utilizando os valores
observados no conjunto de treino
2º Caso
: Se a estrutura da rede bayesiana fôr conhecida, e algumas
das variavéis não podem ser observadas no conjunto de treino.
Então utiliza

se método do
algoritmo do gradiente ascendente
))
(
Pr
/
(
i
i
Y
s
edecessore
y
P
Exemplo 1º caso
Person FH S E LC PXRay D
P1 Sim Sim Não Sim +
Sim
P2 Sim Não Não Sim

Sim
P3 Sim Não Sim Não + Não
P4 Não Sim Sim Sim

Sim
P5 Não Sim Não Não +
Não
P6
Sim Sim ? ? ? ?
LC
~LC
(FH, S)
(FH, ~S)
(~FH, S)
(~FH, ~S)
0.5
…
…
…
…
…
…
…
P(LC = Sim
\
FH=Sim, S=Sim) =0.5
))
(
Pr
/
(
i
i
Y
s
edecessore
y
P
Family
History
LungCancer
Smoker
Emphysema
Exemplo 2º caso
Suppose structure known, variables partially
observable
Similar to training neural network with hidden units
In fact, can learn network conditional probability
tables using gradient ascent
Person FH S E LC PXRay D
P1

Sim

Sim +
Sim
P2

Não

Sim

Sim
P3

Não

Não + Não
P4

Sim

Sim

Sim
P5

Sim

Não +
Não
P6
Sim Sim ? ? ? ?
Summary
Bayesian networks provide a natural
representation for (causally induced)
conditional independence
Topology + CPTs = compact
representation of joint distribution
Generally easy for domain experts to
construct

> P(da,b,c)=P(da,c)=0.66

>
Bayesian networks
Conditional Independence
Inference in Bayesian Networks
Irrelevant variables
Constructing Bayesian Networks
Aprendizagem Redes Bayesianas
Examples

Exercisos
árv dec ID3
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