# What is the point of non-informative priors?

Why even have non-informative priors? They don’t provide information about $\theta$. So why use them? Why not only use informative priors? For example, suppose $\theta \in [0,1]$. Then is $\theta \sim \mathcal{U}(0,1)$ a non-informative prior for $\theta$?

This vision is best represented by Jeffreys’ distributions, where the information matrix of the sampling model, $I(\theta)$, is turned into a prior distribution