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Hey Stefan.<br>
I would expect that to depend on the prior.<br>
It could either be a bug or an issue with the variational inference.<br>
Maybe comparing against an MCMC implementation might be helpful?<br>
Though if that works, I'm not sure what the conclusion would be tbh.<br>
<br>
(I hate debugging variational inference, I can't get the hang of it)<br>
<br>
Can you check the estimated covariance? what is it?<br>
The samples that you're showing are from all 100 components, right?<br>
<br>
Cheers,<br>
Andy<br>
<br>
<div class="moz-cite-prefix">On 2/6/19 1:34 PM, Stefan Ulbrich via
scikit-learn wrote:<br>
</div>
<blockquote type="cite"
cite="mid:CAHQRxX_BN_Wuc8cfbq7XmgziLq8Ykg9ODCZtp0QMRR1gY7hdJg@mail.gmail.com">
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<div dir="ltr">Hello,
<div><br>
</div>
<div>I think I might have found a bug in the
BayesianGaussianMixture–or at least encountered a behavior
that I was not expecting. The problem occurs when having
clusters with small extent (in my case, it is 2D geographic
data) that are far away from each other. While the means and
their number are determined correctly, the co-variance
matrices are not (at least compared to the regular GMM): They
are are much wider and point towards the mean of the cluster
centers. </div>
<div>A minimal example and visualization can be seen on a
stackoverflow question I opened.</div>
<div><br>
</div>
<div><a href="https://stackoverflow.com/q/54524283"
target="_blank" moz-do-not-send="true">https://stackoverflow.com/q/54524283</a><br>
</div>
<div><br>
</div>
<div>So my question is whether the results of GMM and BGMM
should be similar or this is the expected behavior (and why)?</div>
<div><br>
</div>
<div>Thanks in advance for an answer and best wishes</div>
<div>Stefan</div>
</div>
<br>
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