Burglary JohnCalls P(B) .001 Alarm A P(J) 90 f .05 Earthquake B t 1 f f f E t f 1 P(A) 95 94 29 .001 MaryCalls P(E) 002 A P(M) 1.70 f .01 Figure 1 - A typical Bayesian network, showing both the topology and the conditional probability tables (CPTS). In the CPTS, the letters B, E, A, J, and M

Operations Research : Applications and Algorithms
4th Edition
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Wayne L. Winston
Chapter17: Markov Chains
Section: Chapter Questions
Problem 16RP
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1. Consider the Bayesian network in the image attached.

If we observe Alarm = true, are Burglary and Earthquake independent? Justify
your answer explaining which of the probabilities involved satisfy the definition of
conditional independence (no need to perform the actual calculation in class).

Burglary
P(B)
.001
Alarm
A P(J)
JohnCalls 1 .90
f.05
Earthquake
B
E
P(A)
t
t
95
1
f
94
f
1
29
f f .001
MaryCalls
P(E)
.002
A P(M)
1 .70
f .01
Figure 1 - A typical Bayesian network, showing both the topology and the
conditional probability tables (CPTS). In the CPTS, the letters B, E, A, J, and M
stand for Burglary, Earthquake, Alarm, JohnCalls, and MaryCalls, respectively.
Transcribed Image Text:Burglary P(B) .001 Alarm A P(J) JohnCalls 1 .90 f.05 Earthquake B E P(A) t t 95 1 f 94 f 1 29 f f .001 MaryCalls P(E) .002 A P(M) 1 .70 f .01 Figure 1 - A typical Bayesian network, showing both the topology and the conditional probability tables (CPTS). In the CPTS, the letters B, E, A, J, and M stand for Burglary, Earthquake, Alarm, JohnCalls, and MaryCalls, respectively.
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