15) Consider the Bayesian Recommendation Letter Network depicted below. This Bayesian Network has nodes for corresponding random variables (1) Difficulty, (2) Intelligence, (3) Grade, (4) SAT and (5) Letter. The network describes a model which relates the intelligence of a student, the difficulty of a course he takes, the grade obtained by the student, the SAT score of the student, and the strength of the recommendation letter obtained by the student from the professor. We denote these random variables with their first letters D,I,G,S and L respectively. Each of the random variables are discrete random variables and take values from a finite domain. The conditional probability tables (CPT) are shown in the figure. For example, the CPT associated with the node for the random variable $G$ has 2 columns associated with the 2 possible values of G namely g0 and g1, and 4 rows corresponding to the combinations of values possible for the direct dependencies of the node. For example, according to the CPT of variable G: P(g0i0,d0)=0.3. What is the value of the full joint probability ? - 0.118 - 0.014 - 0.006 - 0.212