The file 'diseaseNet.mat' contains the potentials for a disease bi-partite belief network, with 20 diseases d1,,d20 and 40 symptoms, s1,, s40. The disease variables are numbered from 1 to 20 and the symptoms from 21 to 60 . Each disease and symptom is a binary variable, and each symptom connects to 3 parent diseases. 1. Using the BRMLtoolbox, construct a junction tree for this distribution and use it to compute all the marginals of the symptoms, i.e., p(si=1). In addition to providing your code, you must display/print the results in your solution paper work. 2. Explain how to compute the marginals p(si=1) (in the previous part) in a way more efficient than using the junction tree formalism. By implementing this method, compare it with the results from the junction tree algorithm from the previous part. 3. Symptoms 1 to 5 are present (state 1 ), symptoms 6 to 10 not present (state 2 ), and the rest not known. Compute the marginal p(di=1s1:10) for all diseases. In all above questions, in addition to providing your codes, you must display/print the results in your solution paper work