# What is the difference between parametric and non-parametric distributions? [duplicate]

I've learnt that for parametrical distributions you can describe the family of statistical model with the parameters, one such example has been the uniform distribution. I just came across a text saying that the uniform distribution is "inherently non-parametric". What is really the difference between parametric and non-parametric distributions?

• Maybe one way to start looking at this is the difference between distributions that you know the data follows and have a density which is parameterized by a vector $\theta \in \mathbb{R}^n$, $p_\theta(x) = p(x \Vert \theta)$ and models where you can not assume any prior knowledge of the distribution (you can find this under non-parametric density estimation). Maybe this could be a good heads up for you. In this sense the uniform distribution is parameterized though, i.e. by the volume of the set which the distribution is defined on $\lambda(A), A\subset\mathbb{R}^n$, $\lambda$ Lebesguemeasure. Oct 27, 2020 at 12:06