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Mathematics, 04.03.2020 23:37 slonzyy8397

Let y be a random variable with a known distribution, and consider the square loss function `(a; y) = (ay)2. We want to find the action a that has minimal risk, namely, to find a = arg mina E(a y)2, where the expectation is with respect to y. Show that a = Ey, and the Bayes risk (i. e. the risk of a) is Var(y). In other words, if you want to try to predict the value of a random variable, the best you can do (for minimizing expected square loss) is to predict the mean of the distribution. Your expected loss for predicting the mean will be the variance of the distribution. You should use the fact that Var(y) = Ey2 (Ey)2.

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