Question 10 2 pts Select all of the following optimization problems are equivalent to finding the weights, w , which maximize the following Gaussian likelihood, IIn_, N(towT (Xn), yl), with the following Zero-mean Gaussian prior on the weights, IV(w|0, BI) where t, y, B E Rl and w, d(x), 0 E R and I E Rdxd is the identity matrix. maxw IIn {-(wo(x) - tn) (yI)(wTo(x) - tn)} - wT (BI)w minw En {(wTo(x) - tn)(VI)(wTo(x) -tn)} + w.(BI)w O maxw En { -(wd(x) - tn) ? ( 71) 1(wid(x) -tn)} - wI(BI) -1w minw En wid(xn) - tallzy + /wIw/13B Ominw En {(wTo(x) - tn)? (yI) (wid(x) -tn)} - w](BI) -1w O minw En | wid(xn) -tol/2 BQuestion 11 2 pts Of the following prior distributions, which would require more data points to overcome the prior and converge to the MLE solution with the same data points? Beta(alpha = 100, beta = 100) Beta(alpha = 10, beta = 10) Beta(alpha = 1, beta = 1) Beta(alpha = .1, beta = .1)