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
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
Related questions
Question
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).
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