# Which Distribution for Constrained Parameter vector Sum
**URL:** https://discourse.pymc.io/t/which-distribution-for-constrained-parameter-vector-sum/1692
**Category:** Questions
**Created:** [August 9, 2018, 9:20pm UTC](https://discourse.pymc.io/t/which-distribution-for-constrained-parameter-vector-sum/1692 "2018-08-09T21:20:12Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@ded](https://discourse.pymc.io/u/ded)
#### Post date: [August 9, 2018, 9:20pm UTC](https://discourse.pymc.io/t/which-distribution-for-constrained-parameter-vector-sum/1692/1 "2018-08-09T21:20:12Z")
Hi.
I would like to have a vector of K parameters [B1… BK].
The main constraint I have is that their sum is 1.
No particular distribution is needed, except, I would like that the first parameter (B1) be larger than the rest. I can set it to some value.
How should I declare my B param vector?
Thanks in advance
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### Author:  [@ded](https://discourse.pymc.io/u/ded)
#### Post date: [August 10, 2018, 1:23am UTC](https://discourse.pymc.io/t/which-distribution-for-constrained-parameter-vector-sum/1692/2 "2018-08-10T01:23:02Z")
I think I am solving this for now with:
B = pm.Dirichlet(‘proportions’, np.ones(K))
Though, I don’t know yet how to pin the first parameter B[0] to be sufficiently large (~ 0.5)
any idea?
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### Author:  [@junpenglao](https://discourse.pymc.io/u/junpenglao)
#### Post date: [August 10, 2018, 4:33am UTC](https://discourse.pymc.io/t/which-distribution-for-constrained-parameter-vector-sum/1692/3 "2018-08-10T04:33:18Z")
You can control that by playing with the prior parameter of Dirichlet [https://en.wikipedia.org/wiki/Dirichlet\_distribution](https://en.wikipedia.org/wiki/Dirichlet_distribution)