Fraser Information

The Fraser information is

$$ I_F(\theta) = \int g(X) \ln f(X;\theta) , \mathrm d X. $$

When comparing two models, $\theta_0$ and $\theta_1$, the information gain is

$$ \propto (F(\theta_1) - F(\theta_0)). $$

The Fraser information is closed related to [[Fisher information]] Fisher Information Fisher information measures the second moment of the model sensitivity with respect to the parameters. , Shannon information, and [[Kullback information]] KL Divergence Kullback–Leibler divergence indicates the differences between two distributions ¹.