# Skewed distribution

**URL:** <https://discourse.mc-stan.org/t/skewed-distribution/1383>\
**Category:** Modeling\
**Created:** [July 25, 2017, 8:23am UTC](https://discourse.mc-stan.org/t/skewed-distribution/1383 "2017-07-25T08:23:20Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![avehtari](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/avehtari/32/5935_2.png) [@avehtari](https://discourse.mc-stan.org/u/avehtari)\
**Post date:** [July 26, 2017, 8:28am UTC](https://discourse.mc-stan.org/t/skewed-distribution/1383/6 "2017-07-26T08:28:42Z")

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Maybe this helps

> [@Ideas for modeling systematically skewed outliers?](http://discourse.mc-stan.org/t/ideas-for-modeling-systematically-skewed-outliers/636/7):
>
> Here’s a quick implementation of Skewed generalised t (see, e.g. [https://cran.r-project.org/web/packages/sgt/vignettes/sgt.pdf](https://cran.r-project.org/web/packages/sgt/vignettes/sgt.pdf)). There’s no argument checking, but in my experiments I used such constraints for parameters that it was valid all the time. The priors below are weakly informative for my specific case. In your case you might want to keep the parameter p fixed (providing skewed t distribution), and constrain lambda (l) to be negative (to constrain the long tail towards smaller values). …

Aki

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