Consider the below Bayesian Network and answer the following questions: CP(S) I .10 f.50 Sprinkler P(C)=5...

 

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Consider the below Bayesian Network and answer the following questions: CP(S) I .10 f.50 Sprinkler P(C)=5 Cloudy Wet Grass SRP(W) .99 .90 .90 ff.00 Rain CP(R) 180 20 [3+4+1=8 Marks] a. In a past date if it was observed that the grass was wet, then with this additional evidence, is it necessary to check if sprinkler had been on/used to determine if it was cloudy on that day? Justify your answer with appropriate algorithm as discussed in class. b. What is the probability that in a particular day the sprinkler was on/used as well as it rained? Apply approximate inference for this query. Use prior sampling and only 5 samples generated using the below random numbers. "0.3, 0.6, 0.2, 0.1, 0.7, 0.6, 0.6, 0.25, 0.45, 0.85, 0.35, 0.95, 0.15, 0.65, 0.51, 0.2, 0.7, 0.10, 0.4, 0.8" b. Give any one plagiarism free example of application of Bayesian Networks in image processing. Consider the below Bayesian Network and answer the following questions: CP(S) I .10 f.50 Sprinkler P(C)=5 Cloudy Wet Grass SRP(W) .99 .90 .90 ff.00 Rain CP(R) 180 20 [3+4+1=8 Marks] a. In a past date if it was observed that the grass was wet, then with this additional evidence, is it necessary to check if sprinkler had been on/used to determine if it was cloudy on that day? Justify your answer with appropriate algorithm as discussed in class. b. What is the probability that in a particular day the sprinkler was on/used as well as it rained? Apply approximate inference for this query. Use prior sampling and only 5 samples generated using the below random numbers. "0.3, 0.6, 0.2, 0.1, 0.7, 0.6, 0.6, 0.25, 0.45, 0.85, 0.35, 0.95, 0.15, 0.65, 0.51, 0.2, 0.7, 0.10, 0.4, 0.8" b. Give any one plagiarism free example of application of Bayesian Networks in image processing.