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Lecture 8

Lecture 8. Generalized Linear Models & Iterated Reweighted Least Squares (IRLS) Algorithm. Exponential Family. Moments & canonical parameters representation for EFD. Sufficiency: T(x) is all there is to know about parameters. ML estimation: moments are simply average SS.

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Lecture 8

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  1. Lecture 8 Generalized Linear Models & Iterated Reweighted Least Squares (IRLS) Algorithm

  2. Exponential Family • Moments & canonical parameters representation for EFD. • Sufficiency: T(x) is all there is to know about parameters. • ML estimation: moments are simply average SS. • Generalized Linear Models for discriminative Supervised L. - p(Y|X) = expFamDistr. - conditional mean = f(z) f = link func. or response func. - z = a’*x (linear) • Canonical link function gives simple MLE problem (linear in x) • Online gradient descent algorithm.

  3. IRLS • Do Newton-Ralphson iterations. • Updates become like solving a weighted least squares problem, with weights changing at each iteration. • example: logistic regression • demo_LogReg

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