\f{70 points} Bayesian Decision Theory is a fundamental statistic approach to the problem of pattern classifi- cation. It assumes that the decision problem (classification) is posed in probabilistic terms (find out the most probable class), and all relevant probabilities values are known. Give your own UNIQUE and ORIGINAL example of application: (a) {10 points} Define the problem, the classes and the question to answer (b) {5 points} Fix the prior probability of all classes (c) {15 points} Fix the likelihood or class probability for all classes (d) {40 points} Calculate interesting posterior probability to answer your initial questionExercise: Variable Elimination p(smart) = 0.8 p(study) = 0.6 p(fair) = 0.9 " PM...) "u _-E!- Query: What is the prnbability that a student is smart, given that they pass the exam