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 E1 (Un – aw)². The probabilistic model assumes that Lmse (w) = w aw + En, where En is a "noise variable" that describes the 2N Yn prediction error. What conditions are necessary for the equivalence? 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.
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 E1 (Un – aw)². The probabilistic model assumes that Lmse (w) = w aw + En, where En is a "noise variable" that describes the 2N Yn prediction error. What conditions are necessary for the equivalence? 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.
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 33EQ
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