# Idea In [[Bayesian statistics]], we use uniform distributions to represent flat [[posterior|prior distributions]]. When we think that a parameter $\theta$ can take on any value from 0 to 1, we use the notation $\theta \sim Uniform(0, 1)$. Note that the [[beta distribution]], $\theta \sim Beta(1, 1)$ is equivalent to $\theta \sim Uniform(0, 1)$. # References