Q2. We take n independent samples 931, 9:2, . . . ,ccn from the distribution 011 as: a: a a: 2 ex . ) A\" pi (A) l The goal is estimating the parameters A and (1 based on the observed samples $1,932, . . . ,asn. Unlike the previous question where we were able to derive closed form expressions for the ML estimate, for this problem we would not be able to do such thing, and need to consider a numerical minimization technique. Let's go through this task step by step. (a) Formulate the likelihood function f(:c1,...,:c,,|a,)\\)=... (b) Formulate the negative loglikelihood function L()\\,Od) = -10g(f($1, - - - 7$n|aa A ' To estimate the ML estimates 5x and (31 we can either maximize the log likelihood function, or equivalently minimize the negative log likelihood function L()\\, a). For this purpose we decide to use a gradient descent scheme. Derive the expression for the gradient components below: 3L am an\