Bayesian Logistic Regression

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Abstract

As a quick review, the logistic regression model gives the probability of a binary label given a feature vector: P(y=1 | x, w) = σ(w>x) = 1/(1 + e−wx). (1) We usually add a bias parameter b to the model, making the probability σ(w>x+b). Although the bias is often dropped from the presentation, to reduce clutter. We can always work out how to add a bias back in, by including a constant element in the input features x.