Timeline for Mixture Model with dependant observations
Current License: CC BY-SA 3.0
7 events
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| Apr 13, 2017 at 12:58 | history | edited | CommunityBot |
replaced http://mathoverflow.net/ with https://mathoverflow.net/
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| May 18, 2015 at 11:22 | comment | added | demodw | By chance, do you know of any software implementing a model like the one you described above with Normal-Inverse-Wishart? I tried skimming over the literature, but using DP-MM or conjugate inference for multivariate normal as keywords did not really yield anything. | |
| May 13, 2015 at 10:55 | comment | added | conjectures | If you look up Dirichlet process mixture models you'll find some more explanation, but in the setting of not knowing how many components to use. Also looking up conjugate inference for multivariate normal with unknown mean and covariance. | |
| May 13, 2015 at 10:53 | comment | added | conjectures | At a first glance the docs for that package look to me as though they treat each observation as independent once the mixture component is fixed. | |
| May 13, 2015 at 10:45 | comment | added | demodw | I forgot to add, When we fit the GMM previously, we just used all of the data without any pooling. I am not sure if it was fit using a multivariate or univariate. We used the variational implementration of a GMM in sklearn (scikit-learn.org/stable/modules/dp-derivation.html) | |
| May 13, 2015 at 9:36 | comment | added | demodw | Interesting. Do you have any core literature on those kind of mixture models? A quick google search did not provide anything really useful. | |
| May 13, 2015 at 0:02 | history | answered | conjectures | CC BY-SA 3.0 |