 Solution

In a generalized linear model, the linear predictor is ${\eta }_i=x_i^T\beta$, a linear combination of the covariates with unknown coefficients. The link function relates this predictor to the mean: $g({\mu }_i)={\eta }_i$. The canonical link function chooses the natural parameter of the exponential family, namely $g(\mu )=\theta (\mu )$. Differentiating the normalization of the density shows that $\mu =K^{\prime }(\theta )$, so the canonical link function is $(K^{\prime }{)}^{-1}$ on the admissible parameter interval.

For a Bernoulli distribution, the probability mass function can be written asThus $\theta =\log (\mu /(1-\mu ))$ and $K(\theta )=\log (1+e^{\theta })$. The canonical link is the logit: