For the following problem, select all answers that are correct. Under certain conditions, minimizing mean-squared error for linear regression is equivalent to maximizing the log-likelihood of a probabilistic model. As a reminder, the mean-squared error is defined as (Yn xw)². The probabilistic model assumes that Lmse (w) = Yn aw + en, where &n is a "noise variable" that describes the prediction error. What conditions are necessary for the equivalence? 2N Ln= A) The noise parameter €, should follow a normal distribution. B) The parameter N follows an Einstein distribution C) The conditional probability p (yn |æn, w) should follow a Poisson distribution. D) The conditional probability p(yn|æn , w) should follow a Gaussian distribution. O A OC

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