please solve this

14. Expectation-Maximization [8 points] Here we are estimating a mixture of two Gaussians via the EM algorithm. The mixture distribution over x is given by P(r; 0) = P(1)N(r; M1. ]) + P(2)N(r; #12, 0;) Any student in this class could solve this estimation problem easily. Well, one student, devious as they were, scrambled the order of figures illustrating EM updates. They may have also slipped in a figure that does not belong. Your task is to extract the figures of successive updates and explain why your ordering makes sense from the point of view of how the EM algorithm works. All the figures plot P(1) N(r; my, of) as a function of x with a solid line and P(2) N(r; 42, o;) with a dashed line. (a) [2 points] (True/False) In the mixture model, we can identify the most likely T posterior assignment, i.e., j that maximizes P(j | x), by comparing the values of P(1)N(r; /1, of) and P(1)N(T; (2. 0; ) 016 c) (b) [2 points] Assign two figures to the correct steps in the EM algorithm. Step 0: ( ) initial mixture distribution - Step 1: ( ) after one EM-iteration (c) [4 points] Briefly explain how the mixture you chose for "step 1" follows from the mixture you have in "step (&quot