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A standard linear regression assumes the outcome is continuous and normally distributed, which just doesn’t hold up in many of these cases. That’s where GLMs come in.

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Tackling a wide range of optimization problems.

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Intuition for Unifying Theory of GLMs with Derivations in Canonical and Non-Canonical Forms

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Generalized Linear Models (GLMs) play a critical role in fields including Statistics, Data Science, Machine Learning, and other computational sciences. In Part I of this Series, we provided a…