# How to infer components of mixture?

**URL:** <https://discourse.pymc.io/t/how-to-infer-components-of-mixture/5313>\
**Category:** Questions\
**Created:** [June 23, 2020, 7:54am UTC](https://discourse.pymc.io/t/how-to-infer-components-of-mixture/5313 "2020-06-23T07:54:42Z")\
**Posts on this page:** 1\
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**Author:** ![Jandsy](https://avatars.discourse-cdn.com/v4/letter/j/91b2a8/32.png) [@Jandsy](https://discourse.pymc.io/u/Jandsy)\
**Post date:** [June 26, 2020, 2:15pm UTC](https://discourse.pymc.io/t/how-to-infer-components-of-mixture/5313/7 "2020-06-26T14:15:32Z")

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The B\_i are not observed. I observe only rank, and I assume that rank are generated indirectly by mixture. For moment my data (as in [Order statistics in PyMC3](https://discourse.pymc.io/t/order-statistics-in-pymc3/617)) is :

> K = 4 # number of items being ranked  
> J = 1 # number of raters  
> yreal = np.argsort(np.random.randn(K, 1), axis=0)  
> print(yreal)  
> y = np.argsort(yreal + 2\*np.random.randn(K, J), axis=0)  
> print(y)

But in fact I want to be more fleaxible with the data simulation process, that’s why I’ve choosen latent Mixture instead of latent Normal.

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