Arobotic machine’s software uses Bayesian filter to measure its intelligence in processing legal cases to determine whether a judgement made by judges are fair or not.A set of cases were fed into the memory of the robot for processing in determining fairness. Let P (J) =0.25 be the probability of cases brought to the courts with judgement made. Let P(E/J) =0.33 be the probability that a case that was judged was based on evidences presented before the court. Let P (E/J') =0.87 be the probability that some evidences were not used in the judgement even though they were resented before the court. Assuming you are part of the research team that induces the intelligence into the machine, solve the following questions below. 1. Draw the Bayesian Network diagram in a form of a tree diagram to form the basis of reasoning for the machine for the above situation  2. Calculate for the probability for the cases for which judgement has not been passed P (J’) 3. What is the probability for the cases for which judgement has not been passed and has no considerable number of evidences P(E’/J’)?

College Algebra
7th Edition
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:James Stewart, Lothar Redlin, Saleem Watson
Chapter9: Counting And Probability
Section9.3: Binomial Probability
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Arobotic machine’s software uses Bayesian filter to measure its intelligence in processing legal
cases to determine whether a judgement made by judges are fair or not.A set of cases were fed
into the memory of the robot for processing in determining fairness. Let P (J) =0.25 be the
probability of cases brought to the courts with judgement made. Let P(E/J) =0.33 be the
probability that a case that was judged was based on evidences presented before the court. Let P
(E/J') =0.87 be the probability that some evidences were not used in the judgement even though
they were resented before the court.
Assuming you are part of the research team that induces the intelligence into the machine, solve
the following questions below.
1. Draw the Bayesian Network diagram in a form of a tree diagram to form the basis of
reasoning for the machine for the above situation 
2. Calculate for the probability for the cases for which judgement has not been passed P (J’)
3. What is the probability for the cases for which judgement has not been passed and has no
considerable number of evidences P(E’/J’)? 
4. What is the probability for the cases for which judgement were made on but did not
account for the evidences filed against the accused. P (E’/J)?
5. What will be the total probability of evidence of presented cases in the courteven if they
are considerable but were not considered and also not considerable but were considered.
P(E)?
6. What will be the total probability of evidence not presented in all casesP(E’)?
7. What will be the total probability of P(J’/E)?
8. What will be the total probability of P(J/E)?
9. What will be the total probability of P(J’/E’)?
10. What will be the total probability of P(J/E’)?

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