3. Suppose we want to fit a model, y= f(1,5) = Br?. We get data...
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3. Suppose we want to fit a model, y= f(1,5) = Br?. We get data (fi, Yi), i = 1, ...,N and compute the estimate, = N i=1 Yi 2:41 Note: This is not optimal least-squares estimator. But, it is easier to analyze. For each case below compute the bias, Bias(1) := E(F(1,B)) f(x,Bo), as a function of the test point r, true parameter Bo and test data li. = 1 = (a) The training data has no noise: Yi = f(li, Bo). (b) The training data is yi = f(Ti, Bo) + ; where the noise is i.i.d. ; ~ N(0,0%). ( (c) The training data is yi = f(fi + i, Bo) where the noise is i.i.d. ;~ N(0,0%). fI; ) =
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