# Non-centred parameterisation for Dirichlet distribution

**URL:** <https://discourse.mc-stan.org/t/non-centred-parameterisation-for-dirichlet-distribution/1843>\
**Category:** Modeling\
**Created:** [September 10, 2017, 4:38pm UTC](https://discourse.mc-stan.org/t/non-centred-parameterisation-for-dirichlet-distribution/1843 "2017-09-10T16:38:46Z")\
**Posts on this page:** 1\
**Showing post:** 10

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**Author:** ![aaronjg](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/aaronjg/32/3768_2.png) [@aaronjg](https://discourse.mc-stan.org/u/aaronjg)\
**Post date:** [June 14, 2018, 2:41am UTC](https://discourse.mc-stan.org/t/non-centred-parameterisation-for-dirichlet-distribution/1843/10 "2018-06-14T02:41:47Z")

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Check out this thread - it’s on sum to zero constraints, but may be relevant for the sum to 1 constraint.

> [@Test: Soft vs Hard sum-to-zero constrain + choosing the right prior for soft constrain](http://discourse.mc-stan.org/t/test-soft-vs-hard-sum-to-zero-constrain-choosing-the-right-prior-for-soft-constrain/3884/2):
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> I think the first question is why the soft constraint gets such different results from the hard constraint. Yet the result from the soft constraint doesn’t seem in any way sensitive to how hard the constraint is. I don’t know why 0.001 \* ncat is the magic scale. That’s why I suggested posting here. I was hoping @anon75146577 or @mitzimorris or @avehtari would jump in. It’s related to the spatial ICAR model soft smoothing. I have to imagine it has to be set in balance to the parameter scale…

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