4. bayesian estimation bookmark this page setup: in this problem, we will explore the intersection of bayesian and frequentist inference. let , , ⋯ , ∼i. i.d ,theta) , for some unknown positive number theta , which is our parameter of interest. suppose that we are unable to come up with a prior distribution for theta . (a) 1 point possible (graded) compute the maximum likelihood estimator of theta . you may use the variables , ∑= , and ∑= . (enter sigma_i(x_i) for ∑= and sigma_i(x_i^2) for ∑= . do not worry if the parser does not render properly; the grader works independently. if you wish to have proper rendering, enclose sigma_i(x_i) by brackets. )