# Bayesian networks

Τεχνίτη Νοημοσύνη και Ρομποτική

7 Νοε 2013 (πριν από 4 χρόνια και 7 μήνες)

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Bayesian networks

Chapter 14

Slide Set 2

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
)

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)
?

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

9

Using a Bayesian Network

Suppose you want to calculate:

P(A = true, B = true, C = true, D = true)

= P(A = true) * P(B = true | A = true) *

P(C = true | B = true) P( D = true | B = true)

= (0.4)*(0.3)*(0.1)*(0.95)

A

B

C

D

10

Using a Bayesian Network
Example

Using the network in the example, suppose you want to
calculate:

P(A = true, B = true, C = true, D = true)

= P(A = true) * P(B = true | A = true) *

P(C = true | B = true) P( D = true | B = true)

= (0.4)*(0.3)*(0.1)*(0.95)

A

B

C

D

This is from the
graph structure

These numbers are from the
conditional probability tables

11

Inference

Using a Bayesian network to compute
probabilities is called inference

In general, inference involves queries of the
form:

P( X | E )

X = The query variable(s)

E = The evidence variable(s)

12

Inference

An example of a query would be:

P(
HasAnthrax = true

|
HasFever = true
,
HasCough

= true
)

Note: Even though
HasDifficultyBreathing

and
HasWideMediastinum

are in
the Bayesian network, they are not given values in the query (ie. they do not
appear either as query variables or evidence variables)

They are treated as unobserved (hidden) variables

HasAnthrax

HasCough

HasFever

HasDifficultyBreathing

HasWideMediastinum

13

The Bad News

Exact inference in BBNs is NP
-
hard

Though feasible for singly
-
connected networks

But we can achieve significant improvements (e.g., variable elimination)

There are approximate inference techniques which are much faster and
give fairly good results

Next class: inference

Example

In your local nuclear power plant, there is an alarm that senses when a
temperature gauge exceeds a given threshold. The gauge measures the
temperature of the core. Consider the Boolean variables
: A (alarm
sounds), FA (alarm faulty), FG (gauge is faulty),
and the multivalued
variables
G (gauge reading)

and
T (actual core temperature.

The gauge is more likely to fail when the core temperature gets too high

Let’s draw the network (in class)

Example (cont)

Suppose

there are just two possible actual and measured temperatures, normal and high

The
prob

that the gauge gives the correct temp is X when it is working, but Y when it is faulty.
Give the CPT for G (in class)

Suppose

The alarm works correctly unless it is faulty, in which case it never sounds. Give the CPT for
A (in class)

Example (cont.)

Suppose FA=false; FG=false (the alarm and gauge are working properly);
and A=True (and the alarm sounds). What is the probability that the
temperature is too high? (T=high?) (in class)