bv_cvxbook_extra_exercises

m i 1 p with variables m1 mp

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Unformatted text preview: ormcdf helpful for this problem. You can form A using the commands A = sparse(1:m,train(:,1),train(:,3),m,n) + ... sparse(1:m,train(:,2),-train(:,3),m,n); (c) Use the maximum likelihood estimate a found in part (b) to predict the outcomes of next ˆ ˆ year’s tournament games, given in the matrix test, using y (i) = sign(ˆj (i) − ak(i) ). Compare ˆ a these predictions with the actual outcomes, given in the third column of test. Give the fraction of correctly predicted outcomes. The games played in train and test are the same, so another, simpler method for predicting the outcomes in test it to just assume the team that won last year’s match will also win this year’s match. Give the percentage of correctly predicted outcomes using this simple method. 6.5 Estimating a vector with unknown measurement nonlinearity. (A specific instance of exercise 7.9 in Convex Optimization.) We want to estimate a vector x ∈ Rn , given some measurements yi = φ ( a T x + v i ) , i i = 1, . . . , m. H...
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This note was uploaded on 09/10/2013 for the course C 231 taught by Professor F.borrelli during the Fall '13 term at University of California, Berkeley.

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