# Automated distribution fitting **URL:** https://discourse.julialang.org/t/automated-distribution-fitting/30161 **Category:** Statistics **Created:** [October 22, 2019, 10:20am UTC](https://discourse.julialang.org/t/automated-distribution-fitting/30161 "2019-10-22T10:20:00Z") **Posts on this page:** 1 **Showing post:** 4

### Author: ![Tamas\_Papp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tamas_papp/32/25949_2.png) [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp) #### Post date: [October 22, 2019, 11:29am UTC](https://discourse.julialang.org/t/automated-distribution-fitting/30161/4 "2019-10-22T11:29:12Z")

In addition to @anon92994695’s excellent answer, also note that there is an infinite number of distributions; it’s just that some of them have a name and a parametric family for historical/convenience reasons. Any increasing function F: \mathbb{R} \to [0,1] (with some technical properties related to measurability) defines a distribution. People usually use “named” distributions/families as building blocks. Depending on your problem, I would either recommend 1. a simple transformation to the real line (eg with logit) and fitting (a family) of normals: this you can do directly with Distributions.jl, but you need to make sure there is no mass near the endpoints, 2. a Bayesian approach. What you end up doing will depend on how much you care about modeling your data correctly. If you are working at a company, I would suggest hiring a statistician do to this; otherwise read up on some methodology. Some recommendations in an earlier topic starting here: > [@Data Science with Julia](https://discourse.julialang.org/t/data-science-with-julia/19941/14): > > Thank you for your reply! Extremely helpful. Do you have any recommendations for reading materials? It takes a lot of courage to ask for help so I appreciate the kind words and non presumptuous response. --- _[View the full topic](https://discourse.julialang.org/t/automated-distribution-fitting/30161)._