Consider the Bayesian network below. Assume that each variable is boolean, and that a conditional probability table is specified for each node. What is P(I=t|W=f)? t= true f= false I=t I=f Select one: a. 0.93 P(H=t| I) P(H=f | I) 0.8 0.2 0.1 0.9 b. 0.75 c. 0.17 d. 0.34 e. Holmes Crash 0.68 Icy Watson Crash P(I=t) P(I=f) 0.7 0.3 I=t I=f P(W=t| I) P(W=f | I) 0.8 0.1 0.2 0.9

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