# Gaussian mixture with covariance matrix via klqp method

**URL:** <https://discourse.edwardlib.org/t/gaussian-mixture-with-covariance-matrix-via-klqp-method/277>\
**Category:** General\
**Created:** [August 2, 2017, 2:25pm UTC](https://discourse.edwardlib.org/t/gaussian-mixture-with-covariance-matrix-via-klqp-method/277 "2017-08-02T14:25:26Z")\
**Posts on this page:** 1\
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**Author:** ![ecosang](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.edwardlib.org/ecosang/32/110_2.png) [@ecosang](https://discourse.edwardlib.org/u/ecosang)\
**Post date:** [January 29, 2018, 9:29pm UTC](https://discourse.edwardlib.org/t/gaussian-mixture-with-covariance-matrix-via-klqp-method/277/5 "2018-01-29T21:29:57Z")

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Hello, I’ve been away from this problem for a while.

GMM is known as solved by varaitional inference.  
But, in many cases, the update of lower bound is given in a close form such as [http://scikit-learn.org/stable/modules/dp-derivation.html](http://scikit-learn.org/stable/modules/dp-derivation.html) . And I guess this is the reason it works.

Mixture problem might be difficult in KLQP approach since it is almost black box algorithm.  
By the way, when I use ADVI in STAN, it gave a fairly better result. Guess you can improve this with the transformation of distribution? But, I am not very familiar with how this works in detail.

> [@Mean Field ADVI in Edward](https://discourse.edwardlib.org/t/mean-field-advi-in-edward/362):
>
> The class of problems I’m working on (bayesian structural time series), work pretty nicely with ADVI (such as pymc3’s) out of the box, and with the existing inferences in Edward, I’m finding myself tweaking things more often than I’d like. In particular, estimating scale parameters on time series uncertainty, I’m having trouble getting the correct estimates at all. So I’m planning on implementing ADVI in Edward. Beyond following the Kukucelbir et al (2016) paper and existing implementations in …

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